International Joint Conference on Rough Sets

BOOK OF ABSTRACTS
Organized by the International Rough Set Society and the MSc in Computing
and Data Analytics (MCDA) Program at Saint Mary’s University.

Friday, May 17th - Monday, May 20th, 2024
Location: Saint Mary’s University, Halifax, Nova Scotia, Canada (hybrid)

Book of Abstracts

International Joint Conference on Rough Sets

Halifax, Nova Scotia,
Canada2024

https://ijcrs24.cs.smu.ca/

Editors
Xiaodong Yue, Shanghai University, China.
Yan Zhang, California State University San Bernardino, USA.
Mengjun Hu, Saint Mary’s University, Canada.
Chris Cornelis, Ghent University, Belgium.
Pawan Lingras, Saint Mary’s University, Canada.
Dominik Ślęzek, QED Software, Poland.
JingTao Yao, University of Regina, Canada.

TEXnical Editing and Front Cover Design
Zihao Li, Shanghai University, China.

Preface

This book comprises the extended abstracts selected for presentation at IJCRS
2024, the 2024 International Joint Conference on Rough Sets, held at SaintMary’s University in Halifax, Canada, on May 17-20, 2024. The annual IJCRS
series conferences combine four distinct conferences linking rough sets to various
paradigms: RSCTC (data analysis), RSFDGrC (granular computing), RSKT
(knowledge technology), and RSEISP (intelligent systems). The first Joint RoughSet Symposium took place in Toronto, Canada, in 2007; followed by Symposiums
in Chengdu, China (2012); Halifax, Canada (2013); Granada and Madrid, Spain(2014); Tianjin, China (2015), where the acronym IJCRS was proposed; andsubsequent conferences IJCRS 2016 in Santiago, Chile; IJCRS 2017 in Olsztyn,
Poland; IJCRS 2018 in Quy Nhon, Vietnam; IJCRS 2019 in Debrecen, Hungary;
IJCRS 2020 in La Habana, Cuba (online); IJCRS 2021 in Bratislava, Slovakia(hybrid); IJCRS 2022 in Suzhou, China (hybrid); and IJCRS 2023 in Kraków,
Poland (hybrid).
IJCRS 2024 continued to receive significant attention from researchers in
the rough sets community. We received 53 full-length paper submissions and
40 top-quality submissions were accepted after a rigorous single-blind reviewing process. The scientific discourse at IJCRS 2024 was complemented by ten
extended abstracts, describing ongoing work or research published elsewhere in
the past year. These extended abstracts were rigorously reviewed by Program
Committee Chairs and compiled into this Book of Abstracts edited by the Publication Chair, Xiaodong Yue, and his PhD student Zihao Li. The success ofthe conference owes much to the contributions of the authors, reviewers, andProgram Committee Members.
The IJCRS 2024 program featured nine invited talks, including three presentations by former presidents of the International Rough Set Society, Duoqian
Miao, Shusaku Tsumoto, and Wojciech Ziarko, and six keynote talks by renowned
researchers in the field, Lipika Dey, Jimmy Huang, Ryszard Janicki, Eric Matson, Jesus Medina, and Jaroslaw Was. We are grateful to all the invited speakers
for their visionary talks on research related to rough sets. The IJRCS 2024 also
hosted two workshops on “Uncertainty, Three-way Decision, and Explainable Artificial Intelligence” and “Applications of Deep Learning and Soft Computing”
and two special sessions on “General Rough Set Perspectives on Foundations ofAI and Machine Learning” and “Formal Concept Analysis, General Operators
and Related Topics”. Our gratitude is extended to all the workshop and special session chairs, Duoqian Miao, Jianfeng Xu, Chuanlei Zhang, Ying Yu, Hong
Yu, Raavee Kadam, A Mani, Stefania Boffa, Davide Ciucci, Jesús Medina, M.
Eugenia Cornejo, and Eloísa Ramírez-Poussa.
The IJCRS 2024 program was further augmented by Rough Set School andTutorials, and a Data Mining Competition. We are grateful to the chairs, Piotr
Artiemjew, Zaineb Chelly Dagdia, Yasushi Akiyama, and Andrej Janusz, andII

Prefacethe tutorial speakers, Stefania Boffa, James F. Peters, Usman Qamar, Andrzej
Skowron, Dominik Ślęzak, Arkadiusz Wojna, and Yiyu Yao, and the judges andparticipants of the Data Mining Competition.
We appreciate the sponsorship from Springer for the Best Paper Award andthe Best Student Paper Award. The awards were assigned based on a competitive
process, considering scientific excellence and clarity of both articles and presentations. We are also grateful to Jimmy Huang and IEEE for their sponsorship
through the IEEE TCII Fund.
IJCRS 2024 would not have been successful without the support of many
people and organizations. We are grateful to the Program Committee Members and external reviewers for their effort and engagement in providing a richand rigorous scientific program. We greatly appreciate the cooperation, support,
and sponsorship from the MSc in Computing and Data Analytics (MCDA) program at Saint Mary’s University and the International Rough Set Society. We
acknowledge the use of the EasyChair conference system for paper submission
and review. We are also grateful to Springer for publishing the proceedings as a
volume of LNCS/LNAI.
Lastly, thanks are extended to Raavee Kadam, Neelam Pal, Vrushali Prajapati, and other members in the local organizational team for their logistical,
technical, and administrative support, without which IJCRS 2024 would nothave been possible.
May 2024

Mengjun Hu
Chris Cornelis
Yan Zhang
Pawan Lingras
Dominik Ślęzak
JingTao Yao

Organization

Honorary Chairs
James Peters
Andrzej Skowron
Yiyu Yao

University of Manitoba, Canada
Systems Research Institute of Polish Academyof Sciences, Poland
University of Regina, Canada

General Conference Chairs
Pawan Lingras
Dominik Ślęzak
JingTao Yao

Saint Mary’s University, Canada
University of Warsaw, Poland
University of Regina, Canada

Program Committee Chairs
Mengjun Hu
Chris Cornelis
Yan ZhangSaint Mary’s University, Canada
Ghent University, Belgium
California State University San Bernardino,
USA

Steering Committee Chairs
Witold Pedrycz
Roman Słowiński
Guoyin Wang

University of Alberta, Canada
Poznań University of Technology, Poland
Chongqing University of Posts and Telecommunications, China

Data Mining Competition Chairs
Yasushi Akiyama
Andrzej Janusz

Saint Mary’s University, Canada
Chief Data Scientist, QED Software, Poland

Rough Set School and Tutorial Chairs
Piotr Artiemjew
Zaineb Chelly Dagdia

University of Warmia and Mazury in Olsztyn,
PolandUniversité Paris-Saclay, UVSQ, DAVID, France

II

Organization

Special Session and Workshop Chairs
Eloísa Ramírez-Poussa
Hong Yu

Universidad de Cádiz, Spain
Chongqing University of Posts and Telecommunications, China

Publicity Chairs
A ManiStefania Boffa

Indian Statistical Institute, Kolkata, India
University of Milano-Bicocca, Italy

Publication Chair
Xiaodong Yue

Shanghai University, China

Local Organization Chair
Raavee Kadam

Saint Mary’s University, Canada

Administrative Support
Neelam Pal

Saint Mary’s University, Canada

Webmaster
Vrushali Prajapati

Saint Mary’s University, Canada

Program Committee Members
A ManiLubomir Antoni
Roberto G. Aragón
Piotr Artiemjew
Nouman Azam
Jaume Baixeries
Maria-Jose Benitez
Stefania Boffa
Henri Bollaert

Indian Statistical Institute, India
Pavol Jozef S̆afárik University in Kos̆ice, Slovakia
University of Cádiz, Spain
University of Warmia and Mazury, Poland
National University of Computer and Emerging
Sciences, Pakistan
Universitat Politecnica de Catalunya, Spain
University of Cádiz, Spain
Universitá degli Studi di Milano-Bicocca, Italy
Ghent University, Belgium

Organization

III

Nizar Bouguila
Andrea Campagner
Mihir Chakraborty
Shradha Chavan

Concordia University, Canada
Universitá degli Studi di Milano-Bicocca, Italy
Jadavpur University, India
Symbiosis International Deemed University, India

Yingxiao Chen

Beijing University of Posts and Telecommunications, China
Xiamen University of Technology, China
Taiyuan University of Technology, China
National Yang Ming Chiao Tung University,
China
Taiyuan University of Technology, China
Xiamen University of Technology, China
Universitá degli Studi di Milano-Bicocca, Italy
University of Cádiz, Spain
Ghent University, Belgium
University of Debrecen, Hungary
Hunan Normal University, China
Zhejiang Normal University, China
Harbin Engineering University, China
Hacettepe University, Turkey
University of Warmia and Mazury, Poland
University of Warmia and Mazury, Poland
Tunis Higher Institute of Management, Tunisia
Centre National de Recherche Scientifique,FranceSaint Mary’s University, Canada
Iwate Prefectural University, Japan
Shenzhen University, China
University of Białystok, Poland
Nicolaus Copernicus University, Poland
Zhejiang Ocean University, China
VNU-University of Engineering and Technology,
VietnamLoughborough University, England
Saint Mary’s University, Canada
Chongqing University of Posts and Telecommunications, China
Tianjin University, China
McMaster University, Canada
University of Turku, Finland
Aberystwyth University, Wales
Nanjing University of Science and Technology,
China
Fujian university of technology, China

Yumin Chen
Zehua Chen
Mu-Chen Chen
Zehua Chen
Yumin ChenDavide Ciucci
M. Eugenia Cornejo
Chris Cornelis
Zoltán Ernő Csajbók
Jianhua Dai
Dayong Deng
Tingquan Deng
Murat Diker
Pawel Drozda
Soma Dutta
Zied Elouedi
Luis Farinas
Andrew Fisher
Hamido Fujita
Can Gao
Anna Gomolinska
Rafal Gruszczynski
Shen-Ming Gu
Quang-Thuy Ha
Christopher Hinde
Mengjun Hu
Feng Hu
Qinghua Hu
Ryszard Janicki
Jouni Jarvinen
Richard Jensen
Xiuyi Jia
Chunmao Jiang

IV

Organization

Shi Jinyuan
Raavee Kadam
Ting Ke
Michal Kepski
Md. Aquil Khan
Marzena Kryszkiewicz
Yasuo Kudo
Sergei O. Kuznetsov
Tamás Kádek
Guangming Lang
Dajiang Lei
Oliver Urs Lenz
Tianrui Li
Min LiJinhai Li
Huaxiong Li
Jiye Liang
Churn-Jung Liau
Pawan Lingras
Caihui Liu
Guilong Liu
Sujuan Liu
Jiangtao Liu
Junfang Luo
Domingo López-Rodríguez
Vijay Mago
Jesus Medina
Ernestina Menasalvas
Claudio Meneses
Duoqian Miao
Marcin Michalak
Tamás Mihálydeák
Sonajharia Minz
Mikhail Moshkov
Michinori Nakata
Amedeo Napoli

Tianjin University, China
Saint Mary’s University, Canada
Tianjin university of science and technology,
China
University of Rzeszow, Poland
Indian Institute of Technology, India
Warsaw University of Technology, Poland
Muroran Institute of Technology , Japan
National Research University Higher School of
Economics, Russia
University of Debrecen, Hungary
Changsha University of Science and Technology,
China
Chongqing University, China
Leiden University, Netherlands
Southwest Jiaotong University, China
Tianjin University of Science and Technology,
China
Kunming University of Science and Technology,
China
Nanjing University, China
Shanxi University, China
Academia Sinica, China
Saint Mary’s University, Canada
Gannan Normal University, China
Beijing Language and Culture University, China
Tianjin University of Science and Technology,
China
Tianjin University of Science and Technology,
ChinaSouthwestern University of Finance and Economics, China
University of Málaga, Spain
York University, Canada
University of Cádiz, Spain
Universidad Politécnica de Madrid, Spain
Universidad Católica del Norte, Chile
Tongji University, China
Silesian University of Technology, Poland
University of Debrecen, Hungary
Jawaharlal Nehru University, India
King Abdullah University of Science and Technology, Saudi Arabia
Josai International University, Japan
Université de Lorraine, FranceOrganization

Nikita Neveditsin
Thuc Nguyen
Hoang Son Nguyen
Khuong Nguyen-An
Agnieszka Nowak-Brzezinska
Manuel Ojeda-Aciego
Vladimir Parkhomenko

VSaint Mary’s University, Canada
University of Science-VNUHCM, Vietnam
Hue University, Vietnam
Bach Khoa University, Vietnam
University of Silesia, Poland
University of Malaga, Spain
Peter the Great St.Petersburg Polytechnic University, Russia
Andrei Paun
University of Bucharest, Romania
Daniel Peralta
Ghent University, Belgium
Alberto Pettorossi
Università di Roma Tor Vergata, Italy
Małgorzata Przybyła-Kasperek Uniwersytet Śląski w Katowicach, Poland
Jianjun Qi
Xidian University, China
Jin Qian
East China Jiaotong University, China
Sheela Ramanna
University of Winnipeg, Canada
Eloísa Ramírez-Poussa
University of Cádiz, Spain
Marek Reformat
University of Alberta, Canada
Mauricio Restrepo
Universidad Militar Nueva Granada, Colombia
Sergio Ribeiro
Pontificia Universidade Catolica do Parana,
BrazilHenryk Rybiński
Warsaw University of Technology, Poland
Hiroshi Sakai
Kyushu Institute of Technology, Japan
Ambuja Salgaonkar
University of Mumbai, India
B. Uma Shankar
Indian Statistical Institute, India
Marek Sikora
Silesian University of Technology, Poland
Andrzej Skowron
Systems Research Institute of Polish Academyof Sciences, Poland
Dominik Slezak
University of Warsaw, Poland
Roman Slowinski
Poznan University of Technology, PolandŁukasz Sosnowski
Systems Research Institute of Polish Academyof Sciences, Poland
Urszula Stanczyk
Silesian University of Technology, Poland
John Stell
University of Leeds, England
Lin SunTianjin University of Science and Technology,
China
Langwangqing Suo
Shaanxi Normal University, China
Marcin Szczuka
University of Warsaw, Poland
Ryszard Tadeusiewicz
AGH University of Science and Technology,
PolandAdnan Theerens
Ghent University, Belgium
Dan Thu Tran
University of Management and Technology, USA
Bala Krushna Tripathy
Vellore Institute of Technology, India
Li-Shiang Tsay
North Carolina Agricultural and Technical Sate
University, USA

VI

Organization

Bay VoZhen Wang
Ye Wang
Guoyin Wang
Wei WeiJun Xie
Jianfeng Xu
Xin Yang
Hailong Yang
Jilin Yang
Tian Yang
Yiyu Yao
Ning Yao
Minda Yao
JingTao Yao
Hong Yu
Ying Yu
Xiaodong Yue
Yanhui Zhai
Jianming Zhan
Chuanlei Zhang
Yuanjian Zhang
Yan Zhang
Qinghua Zhang
Li Zhang
Hongyun Zhang
Shu Zhao
Huilai Zhi
Bing Zhou
Jie Zhou
Wojciech Ziarko
Beata Zielosko
Jianhang Yu

Ho Chi Minh City University of Technology,
VietnamWeihai Institute of Beijing Jiaotong University,
China
Texas A&M University, USA
Chongqing University of Posts and Telecommunications, China
Shanxi University, China
Taiyuan University of Technology, China
Nanchang University, China
Southwestern University of Finance and Economics, China
Shaanxi Normal University, China
Sichuan Normal University, China
Hunan Normal University, China
University of Regina, Canada
Tongji University, China
China University of Mining and Technology,
China
University of Regina, Canada
Chongqing University of Posts and Telecommunications, China
East China Jiaotong University, China
Shanghai University, China
Shanxi University, China
Hubei Institute for Nationalities, China
Tianjin University of Science and Technology,
China
Shanghai University, China
Cal State University San Bernardino, USA
Chongqing University of Posts and Telecommunications, China
Soochow University, China
Tongji university, China
Anhui University, China
Shanghai University, China
Sam Houston State University, USA
Shenzhen University, China
University of Regina, Canada
University of Sielsia, Poland
Chongqing University of Posts and Telecommunications, ChinaIJCRS 2024

Table of Contents

Table of Contents
Suggestions on the Use of Three-way Decision in Machine Learning . . . . . . . . . . . . . . . . . . . . . . .
Federico Cabitza, Andrea Campagner and Davide Ciucci

1

Privacy-Preserving Federated Learning: Insights and Perspectives . . . . . . . . . . . . . . . . . . . . . . . .
Ankit Chouhan and Jingtao Yao

6Interfacing with a Machine Learning Framework to Detect Drug-Related Harms on
Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Andrew Fisher and Vijay Mago
Generalized multi-adjoint fuzzy rough set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Nicolas Madrid, Jesús Medina and Eloisa Ramı́rez Poussa
Aspect-Based Sentiment Analysis in the LLM Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Nikita Neveditsin, Pawan Lingras and Vijay Mago
Interactive Machine Learning and Data Labeling – Can Rough Sets Help? . . . . . . . . . . . . . . . . 22
Dominik Slezak
Attribute Ranking Method Based on Conditional Comparison Matrices . . . . . . . . . . . . . . . . . . . 26
Bich Khue Vo and Hung Son Nguyen
Cross-domain Structural Damage Identification based on Deep Domain Adaptation
Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Hao Wang, Baoli Wang, Caixia Cui and Pengling Wang
Sequential Three-way Detection Method of Hybrid Concept Drift . . . . . . . . . . . . . . . . . . . . . . . . . 35
Ruihua Wang, Jianfeng Xu and Hong Rao
Representing Pedagogic Content Knowledge Through Rough Sets. . . . . . . . . . . . . . . . . . . . . . .. . 39
A ManiSuggestions on the Use of Three-way Decision in
Machine Learning
Andrea Campagner1 , Davide Ciucci2 , and Federico Cabitza1,2
1

IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
MUDIlab, University of Milano-Bicocca, Italy

21Introduction

The use of Three-Way Decision (TWD) to handle uncertainty in Machine Learning (ML) has attracted a great interest in the last years. In order to categorize
the applications of TWD-based approaches in ML, we distinguish between methods that deal with uncertainty in either the input or the output of a Machine
Learning pipeline [6], as shown in Figure 1.

Fig. 1. A graphical representation of the framework adopted to classify applications ofThree-way decision in Machine Learning.

In the context of machine learning (ML), ”Uncertainty in the input” refers
to situations where training datasets have instances of uncertainty [13]. This
uncertainty can manifest in two primary forms: Incomplete data and weak supervision. Incomplete data happens when certain values of predictive features inthe dataset are missing [18] or not fully available [19, 23].
Weak and multi-source supervision occur when the uncertainty impacts thesupervision process—meaning the target or decision variable—or the relationshipbetween predictive features and supervision is only partially defined. As a result,
weak supervision is the counterpart of incomplete data, with the difference beingthat it affects the target space instead of the feature space [9, 17, 21].
On the other hand, ”uncertainty in the output” describes what is known ascautious AI, a term that has gained popularity to describe AI systems that reveal
their uncertainty instead of making definite, single-value classifications [5, 16].
1

This approach involves using uncertainty quantification techniques to enhance
the robustness of ML models by clearly presenting their predictive uncertainty,
thereby enabling models to abstain from making a decision to some extent.
The use of TWD (Three-Way Decisions) in developing uncertainty quantification methods in ML aims to address both classification and clustering problems.
Initially, TWD was applied to spam detection for classification [24, 27] and later
extended to clustering [26], evolving from concepts such as rough clustering andinterval set clustering. A key recommendation of TWD in these scenarios is toallow classifiers or clusterers to partially abstain [25], meaning they can exclude
some potential classifications, thus enhancing decision-making under uncertainty.
In this extended abstract, based on the above mentioned categorization of applications of TWD in ML, we present the observations and insights drawn from amore recent systematic survey [10] of the specialized literature through which we
studied the application of TWD in ML. In this survey [10] we noted how, despite
the increasing adoption of, and interest toward, TWD in ML, a lack of reporting
standards and attention towards evaluation and reproducibility practices canbe observed, undermining the generalizability and methodological soundness ofreported results in the field. Thus, we here summarize these observations, focusing in particular on presenting recommendations and implications for future
research related to TWD in ML.

2

Discussion

Areas of concern in TWD applied to ML are:
– Issue: a lack of reporting standard, both in regard to data and model aspects. Indeed, a majority of the surveyed studies failed to comprehensively
document the main characteristics of the datasets considered for validation.
Such lack of information can have a severe impact on the reproducibility,and hence credibility, of a study’s results.
Possible solution: to adopt and follow reporting checklists, including both
checklists devoted specifically to data aspects [2] as well as more generalreporting guidelines.
– Issue: many studies (especially so in the weak supervision and missing data
categories) failed to provide sufficient details on crucial aspects of the ML
pipeline, such as hyper-parameter optimization, which could severely impact
on the generalizability and robustness of the reported results.
Possible solution: as in the previous point, more closely follow existing
standard reporting guidelines, such as [3, 11].
– Issue: in regard to validation design, a surprisingly large number of studies
only adopted internal validation study designs: training and testing of a ML
approach are performed on the same set of data. While these designs are notwrong by themselves, their sole application may limit the generalizabilityof the results, if the risks of overfitting and data leakage are not properly
accounted for.
2

Possible solution: A simple approach to ensure the distinct separation oftraining and testing data involves using validation techniques like hold-out
validation or cross-validation, and methods that incorporate randomizationsuch as bootstrapping to mitigate overfitting risks. However, it’s importantto recognize that these validation techniques alone cannot confirm the generalizability of ML techniques in out-of-distribution scenarios [4, 22]. Notably,
none of the studies reviewed utilized external validation. Investigating theeffectiveness of TWD-inspired methods under these conditions represents apromising avenue for future research.
– Issue: In regard to validation metrics, most studies only focused on accuracy, which is, however, not well suited for settings affected by label imbalance. Furthermore, almost none of the surveyed studies considered metrics
that go beyond the measurement of error rate, neglecting important performance dimensions such as calibration [20].
Possible solution: the adoption of reporting guidelines could help TWD
researchers in the selection of appropriate validation metrics.
– Issue in regard to statistical analysis, only a minority of studies assessed
the significance of the observed results.
Possible solution: it is recommended that future works in TWD research
provide more comprehensive statistical analysis of the reported results, adopting approaches either based on hypothesis testing [12] or confidence intervals[1]. Importantly, following recent guidelines on the subject, TWD researchers
should not only report about the significance of results, but focus on providing comprehensive discussion of p-values, effect sizes [15] as well as potential
corrections needed to avoid biases and over-estimation of effects [14].
Concluding, we provide suggestions for future directions of research in the application of TWD to ML:
1. Clustering-based TWD approaches for weakly supervised learning seem toimprove robustness to the curse of dimensionality, especially in comparison
with traditional clustering-based methods. This hypothesis should be further
investigated in future research;
2. TWD-based approaches for missing data management seem to offer a distinct
advantage over traditional techniques adopted in the ML literature, in thatthey do not necessarily require imputation, a data processing step that may
negatively impact the generalizability of ML studies. Future research should
be devoted at exploring this advantage of TWD-based methods, as well as
at comparing them with other ML techniques that likewise do not require
imputation (e.g., missing indicators);
3. For classification tasks, TWD-based methods often blend feature selection
with classification, a practice rooted in the extensive use of rough set-theoretic(RST) techniques in TWD research. This suggests a need for ablation studies to separately evaluate the effects of feature selection and classificationon performance. Moreover, the heavy reliance on RST-inspired techniques
opens up opportunities to investigate other TWD-based strategies that do3not depend on RST, especially those that merge TWD with cautious inference techniques or related methods like conformal prediction or active
learning.
4. In regard to both the classification and clustering tasks, we observed that
only a minority of the surveyed articles properly accounted for the uncertainty quantification properties of TWD-based approaches in the validation
phase. As for the classification task, we believe that future research should
be focused at better exploring the accuracy-coverage trade-off offered bycommonly adopted TWD-based methods, both from an empirical point ofview as well as from a theoretical one [6]. As for the clustering task, we
believe that future studies that more accurately and precisely investigate
their advantages with respect to hard clustering methods, using appropriate
validation metrics [7, 8], are particularly needed;
5. Finally, in regard to clustering, most of the surveyed studies focused on techniques based on generalizing existing partitional (e.g., k-means) and density(e.g., DBSCAN) clustering methods. On the one hand, this suggests thatfurther attention should be focused toward other clustering methods’ families, such as hierarchical clustering. On the other hand, due to three-way
clustering’s ability to more comprehensively represent clustering uncertainty,
a particularly interesting direction for future research would be the investigation of the trade-offs between these different forms of uncertainty.

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Altman, D.G.: Statistical tests, p values, confidence intervals, and power: a guideto misinterpretations. European journal of epidemiology 31, 337–350 (2016)16. Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machinelearning: An introduction to concepts and methods. Machine Learning 110(3),457–506 (2021)
17. Lienen, J., Hüllermeier, E.: Credal self-supervised learning. Advances in Neural
Information Processing Systems 34 (2021)
18. Little, R.J., Rubin, D.B.: Statistical analysis with missing data, vol. 793. John
Wiley & Sons (2019)19. Miao, X., Gao, Y., Guo, S., Liu, W.: Incomplete data management: a survey.
Frontiers of Computer Science 12(1), 4–25 (2018)
20. Ojeda, F.M., Jansen, M.L., Thiéry, A., Blankenberg, S., Weimar, C., Schmid, M.,
Ziegler, A.: Calibrating machine learning approaches for probability estimation: A
comprehensive comparison. Statistics in Medicine 42(29), 5451–5478 (2023)
21. Poyiadzi, R., Bacaicoa-Barber, D., Cid-Sueiro, J., Perello-Nieto, M., Flach, P.,
Santos-Rodriguez, R.: The weak supervision landscape. In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops andother Affiliated Events (PerCom Workshops). pp. 218–223. IEEE (2022)
22. Steyerberg, E.W., Harrell, F.E.: Prediction models need appropriate internal,
internal–external, and external validation. Journal of clinical epidemiology 69,
245–247 (2016)
23. Williams, D., Liao, X., Xue, Y., Carin, L., Krishnapuram, B.: On classification withincomplete data. IEEE transactions on pattern analysis and machine intelligence
29(3), 427–436 (2007)
24. Yao, Y.: Three-way decisions with probabilistic rough sets. Information sciences180(3), 341–353 (2010)
25. Yao, Y.: An outline of a theory of three-way decisions. In: Rough Sets and Current
Trends in Computing. pp. 1–17. Springer Berlin Heidelberg (2012)
26. Yu, H.: A framework of three-way cluster analysis. Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics) 10314 LNAI, 300–312 (2017)
27. Zhou, B., Yao, Y., Luo, J.: A three-way decision approach to email spam filtering.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics) 6085 LNAI, 28–39 (2010)

5

Privacy-Preserving Federated Learning: Insights
and Perspectives
Ankit Chouhan and JingTao Yao
University of Regina, Regina SK S4S 0A2, Canada{ankitchouhan,Jingtao.Yao}@uregina.ca
Abstract. Federated learning is a distributed machine learning approach
where learning occurs directly on devices without central data collection.
This approach processes data where it is generated, significantly enhancing user privacy and system security. This innovative approach promises
substantial benefits across various domains, from healthcare to smart
cities, by enabling collaborative model training while safeguarding sensitive information. Despite its potential, it faces various challenges, such asprivacy preservation, communication efficiency, heterogeneity, and security. The issue of privacy preservation becomes even more complex withthe advent of advanced technologies. There is a need to address these
privacy-preserving challenges to enhance the applicability of federated
learning in real-world scenarios. This work aims at the essence of federated learning, exploring its architecture, data partitioning strategies,
and the pressing need for innovative privacy-preserving methods.
Keywords: Artificial Intelligence · Federated Learning · Privacy-Preserving

1

Introduction

Federated Learning (FL) is a distributed machine learning approach that trains
models across multiple devices or servers holding local data samples, without
exchanging them [4]. This method significantly enhances data privacy, security,
and access across various devices. By addressing data privacy and fragmentation
challenges, FL allows for the collaborative training of models without compromising individual data privacy, strengthening the path for a more trusted andexpansive adoption of technologies [7]. FL is revolutionizing machine learning
by offering a more private and efficient way to train models. This makes FL
especially useful in fields like healthcare and marketing, where data is sensitive
but crucial for developing powerful models. By keeping data on local devices
and reducing reliance on big data centers, FL not only keeps data safer but also
makes machine learning more accessible and scalable, even in areas where data
sharing is restricted [1].
FL is commended for its privacy-preserving capabilities, but it faces its own
set of challenges in maintaining this privacy. The process of exchanging model
information between devices may still create openings for attackers to hack thesystem or manipulate data. Therefore, securing FL against these threats is a
critical area of ongoing research. Additionally, FL must deal with the diversity
6

of data and device capabilities, which can complicate the learning process andimpact the accuracy of models. Communication efficiency is another challenge,
requiring solutions to handle bandwidth limits and the cost of sending data
across networks [6]. These challenges highlight the need for FL to continuously
evolve in addressing security concerns, managing data and device diversity, andimproving communication to maintain its privacy advantages.
Various methods have been developed to address privacy-preserving challenges, including homomorphic encryption [3], differential privacy [2], secure
multi-party computation [4], game-theory and three-way decisions each offering a way to perform computations on encrypted data to prevent the disclosureof sensitive information [10]. However, these methods introduce some limitations,such as increased computational complexity, potential degradation in model performance, and the complex balance between privacy and utility.

(a) Horizontal Federated Learning(b) Vertical Federated Learning(c) Transfer Federated Learning

Fig. 1: Federated Learning Data Partition Strategies

2

Federated Learning: Architecture

In FL, the network architecture is categorized into two main types: centralized
and fully decentralized. The centralized FL architecture typically relies on a single server to aggregate models from participants, enhancing trust and managing
the learning process. In a fully decentralized FL method, there is no need for acentral server to aggregate the models. Instead, it establishes trust and reliability among participants. Unlike centralized models with a single global model, ina decentralized setup, each participant improves their model by sharing information with nearby nodes in a peer-to-peer network [7].
7The architecture of FL encompasses three main data partition strategies:
horizontal federated learning, vertical federated learning, and federated transfer
learning. horizontal federated learning involves datasets on various devices sharing identical features but representing different instances [4]. This form of FL
is particularly suitable for scenarios where clients possess similar feature spaces
as shown in Fig. 1a. Vertical federated learning as shown in Fig. 1b, shareddata from different domains trains a global machine learning model. Usually,an intermediate third party ensures encryption to protect shared data statistics.
In practical scenarios, vertical federated learning proves useful, data features
are divided vertically among parties, with one party usually holding labels andacting as the FL manager. Federated transfer learning as shown in Fig. 1c is
an extension of traditional transfer learning techniques, allowing a pre-trainedmodel to be adapted for solving new problems in federated environments [4].
Existing research on privacy-preserving in FL goes beyond basic techniques
like secure multi-party computation and differential privacy. They explore advanced methods such as the VerifyNet framework and adversarial training [8].
These strategies aim to enhance data protection while keeping the collaborative
learning process effective and secure. The integration of game theory and threeway decision-making further illustrates this trend, providing robust approaches
to privacy preservation. Game theory offers a strategic framework for modelinginteractions among participants, encouraging cooperative behavior, and improving security. Similarly, the three-way decision method allows for dynamic andflexible privacy settings. However, implementing these methods presents challenges, including increased computational demands and potential impacts onmodel performance [4, 7, 10].

3

Concluding Remarks

FL represents a revolutionary approach to machine learning. While it offers many
benefits, it also faces challenges in privacy-preserving. Although existing privacy
mechanisms keep a solid foundation for data protection, advancing technologies
demand innovative solutions to safeguard privacy effectively without losing system performance. Exploring federated learning has revealed many challenges andopportunities, directing researchers to areas that require further investigation.
Future research in the domain of privacy-preserving mechanisms in FL presents
an approach aimed at enhancing both privacy and efficiency while maintaining
the utility of shared data. The exploration of advanced game-theoretical models
promises to develop dynamic and adaptive privacy-protection mechanisms that
respond in real-time to changes in FL environments and participant behaviors,offering robust privacy without compromising model accuracy or high computational costs [10]. The integration of anonymization techniques like differential
privacy and secure multi-party computation into the three-way decision model
could significantly balance privacy, data utility, and computational efficiency [5].
Further, there is a need for developing adaptive and granular privacy measurescapable of adjusting based on real-time assessments of data usage and associ8

ated risks [9]. Moreover, the development of lightweight, efficient FL frameworks
that support real-time processing and decision-making, without sacrificing scalability or adaptability, is essential. Collectively, these future research directions
highlight a comprehensive and integrated approach to advancing the field ofprivacy-preserving federated learning.

References
1. Kaylani Bochie. An analysis of federated learning on mobile networks. Netw.
Comput. Appl., 194:103213–103258, 2021.
2. Oshamah Khalaf Ibrahim, Sameer Algburi, Anupallavi S, Dhanasekaran Selvaraj,
Mhd Saeed Sharif, and Wael Elmedany. Federated learning with hybrid differential
privacy for secure and reliable cross-iot platform knowledge sharing. Security andPrivacy, page e374, 2024.
3. Wenchao Liu, Tanping Zhou, Long Chen, Hongjian Yang, Jiang Han, and Xiaoyuan
Yang. Round efficient privacy-preserving federated learning based on mkfhe. Computer Standards & Interfaces, 87:103773, 2024.
4. Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava. A survey on security and privacy of federated
learning. Future Generation Computer Systems, 115:619–640, 2021.
5. Jin Qian, Haoying Jiang, Ying Yu, Hui Wang, and Duoqian Miao. Multi-level
personalized k-anonymity privacy-preserving model based on sequential three-way
decisions. Expert Systems with Applications, 239:122343, 2024.
6. Prohim Tam, Riccardo Corrado, Chanthol Eang, and Seokhoon Kim. Applicability of deep reinforcement learning for efficient federated learning in massive iot
communications. Applied Sciences, 13(5):3083, 2023.
7. Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb. Federated
machine learning: Survey, multi-level classification, desirable criteria and future
directions in communication and networking systems. IEEE Comm. Surveys &Tutorials, 23(2):1342–1397, 2021.
8. Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. Verifynet: Secure and verifiable federated learning. IEEE Trans. on Information Forensics andSecurity, 15:911–926, 2019.
9. Yang Xu, Md Zakirul Alam Bhuiyan, Tian Wang, Xiaokang Zhou, and Amit Kumar Singh. C-fdrl: Context-aware privacy-preserving offloading through federated
deep reinforcement learning in cloud-enabled iot. IEEE Transactions on Industrial
Informatics, 19(2):1155–1164, 2022.
10. Lihua Yin, Sixin Lin, Zhe Sun, Ran Li, Yuanyuan He, and Zhiqiang Hao. A gametheoretic approach for federated learning: a trade-off among privacy, accuracy andenergy. Digital Communications and Networks, 2023.

9

Interfacing with a Machine Learning Framework
to Detect Drug-Related Harms on Social Media⋆
Andrew Fisher1[0000−0002−4697−1529] and Vijay Mago2
1

1

Saint Mary’s University, Halifax, Canada andrew.fisher@smu.ca
2York University, Toronto, Canada

Introduction

The use of social media platforms has emerged as a primary method for publichealth agencies to disseminate information, including trends related to druguse [2,5]. Recognizing the potential of this data source to act as a real-time
indicator for drug trends across Canada, a study published by our group in
the Journal of Medical Internet Research (JMIR) introduced a novel frameworkthat leverages artificial intelligence (AI) to monitor and analyze content fromTwitter/X [3]. This extended abstract describes a project that expands uponthis framework, aiming to create a comprehensive user interface for both publichealth professionals and the general public, facilitating the tracking and analysis
of drug-related trends on social media.

2

Framework and Motivation

Our previous research represents an effort to automate the identification of
drug-related content on social media by applying zero-shot classification algorithms [1,3]. This initiative arose from a need to enhance public health surveillance mechanisms, enabling quick identification and response to emerging drug
trends [4]. By offering an automated method for detecting drug-related trends,
our framework aimed to facilitate interventions and support evidence-based policy making in the realms of public health as well as substance abuse prevention.

3

Project Motivation and Goals

This abstract builds upon the capabilities of our previous work [3], driven by
the motivation of making nuanced, real-time data on drug trends accessible toboth experts and the general public. It aims to bridge the gap between complex
data analytics and user-friendly information dissemination, thereby providing
individuals with the knowledge necessary to understand substance abuse in theircommunity. By having an informed dialogue around drug trends, the project
strives to contribute to a more proactive and preventive approach in publichealth surveillance.
⋆

Supported by the Canadian Centre on Substance Use and Addiction

104Homepage and Interactive Map View

The platform’s homepage presents users with a dynamic map view, acting as
a visual aid to interpret drug-related trends across different geographical locations. This is achieved by systematically monitoring the social media accounts ofpublic health organizations, police departments, and harm reduction agencies togather any new posts they publish on the Twitter/X platform. Then, the zeroshot classification algorithm from our previous work [3] is applied to assess therelevance of each post to our Early Warning System (EWS). It accomplishes thisby categorizing each post into one of the predefined categories listed in Table 1.
Posts that do not align with these categories are maintained in the database but
hidden from the user interface.
Table 1. The distribution of labels and categories used on the CCSA EWS
Label
PublicHealthPublicSafetyDrug
SupplyMeaning
Category (sub label)
Posts that point to drug-related health increased drug overdoses or risk orrisks, harms, and adverse events, in- poisoning, opioid emergency or crisis
cluding drug poisoning eventsor advisory or overdose signs
Posts that point to drug manufacture drug trafficking or possession, drugand supply routes through law enforce- related seizure or investigation
ment responses
Posts that point to the contents of drug supply or mixing, drug supply
the unregulated drug supply, including alert
adulterants and emergence of NPS

The homepage enables users to interact with the data by applying filters
to narrow their search according to keywords, time frames, and regions. Furthermore, users can vote on the accuracy of each categorization, providing a
feedback mechanism that helps to refine the algorithm further. This tool provides an simple way to navigate through extensive, geographically diverse data,
allowing users to streamline the information and obtain more relevant results.
For further understanding, please refer to Figure 1, which is annotated as follows:
1. Province Filter: This filter can be used to only display tweets from acertain province
2. Label Filter: This filter can be used to only display tweets under one of
the three labels: public health, public safety, or drug supply
3. Time Filter: This filter can be used to only display posts within a certain
time range
4. Summarized Results: This table will display a summary of the posts
as constrained by the filters. The Map View (6) will adjust dynamicallyalongside. The tweet counts can be clicked on to zoom the map into thecorresponding location
5. Visualize Button: Users that are signed into the interface can click on thisbutton to access the analysis tool11

6. Map View: This view displays clusters of posts that meet the current filter
criteria. Moving around the map or changing the zoom level will dynamicallychange the Summarized Results (4) alongside

1

6

2
3

4

5

Fig. 1. An annotated screenshot of the homepage in the proposed interface

Additionally, consider Figure 2 which is annotated as follows:
7. Tweet Text: This column provides the exact results that are currently
visible in the Map View (6) and counted in the Summarized Results(4). Any changes to the filters or map view will update the table that thiscolumn is in
8. Category: This column provides the category for the given tweet text thatwas determined by the zero-shot classification algorithm. The confidence
that the algorithm had in the classification is also provided
9. Accuracy: This column allows users that are signed into the interface toprovide feedback on the categorization. If they feel it is inaccurate, they canclick on the button and will be asked to provide the correct classification.
This information will eventually be used to improve the zero-shot classification algorithm by providing it with examples of inaccurate categorizations
10. Summarized Plot: This plot provides a summative count over time of thelabel distribution in the current map view. The labels at the top of the plot
can be clicked on to hide them from the plot

5

Current Status

The development roadmap includes conducting usability studies to enhance theuser experience, incorporating additional social media platforms to expand thedata collection scope, and integrating diverse media formats, including videos.
12

7

8

9

10

Fig. 2. A continued annotated screenshot of the homepage in the proposed interface

A continuous priority is the improvement of the classification model, with thegoal of increasing the accuracy and relevance of trend detection. Moreover, toprovide more in-depth insights into the collected data, we are exploring the useof Kibana and Elasticsearch, with initial results showcased in a tutorial video on
YouTube1 . These initiatives aim to ensure the platform remains at the forefrontof public health surveillance technology.

6

Conclusion

By integrating the processing capabilities of artificial intelligence into a simple
platform, this extended abstract presents a notable advancement in the field of
public health surveillance. It showcases a combination of data science and user
experience design, rendering it a valuable tool for monitoring and understanding
drug-related trends. As the project advances, its goal is to enhance public healthstrategies through proactive and informed participation from both experts andthe public in understanding substance abuse.

References
1. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán,F., Grave, E., Ott, M., Zettlemoyer, L., Stoyanov, V.: Unsupervised cross-lingual
representation learning at scale. arXiv preprint arXiv:1911.02116 (2019)
2. Fisher, A., Sharma, R., Mago, V.: Analyzing the trends of responses to COVID-19
related tweets from news stations: An analysis of three countries. In: International
Workshop on Health Intelligence. pp. 273–288. Springer (2023)
3. Fisher, A., Young, M.M., Payer, D., Pacheco, K., Dubeau, C., Mago, V.: Automating detection of drug-related harms on social media: Machine learning framework.
Journal of Medical Internet Research 25, e43630 (2023)
4. Higgins, K., O’Neill, N., O’Hara, L., Jordan, J.A., McCann, M., O’Neill, T., Clarke,
M., O’Neill, T., Campbell, A.: Evidence for public health on novel psychoactive
substance use: A mixed-methods study. Public Health Research 7(14), 1–150 (2019)5. Kazemi, D.M., Borsari, B., Levine, M.J., Dooley, B.: Systematic review of surveillance by social media platforms for illicit drug use. Journal of Public Health 39(4),763–776 (2017)1https://www.youtube.com/watch?v=1QdKZSIuo4I

13

Generalized multi-adjoint fuzzy rough set?
Nicolás Madrida , Jesús Medinaa , Eloı́sa Ramı́rez-PoussaaaDepartment of Mathematics, University of Cádiz. Spain
Email: {nicolas.madrid,jesus.medina,eloisa.ramirez}@uca.es

Keywords: Galois connection, Multi-adjoint property-oriented concept
lattice, Rough sets, Approximation operators.
Rough set theory, introduced by Pawlak [14] in the eighties, is a mathematical tool with many applications [2, 6, 7] and relationships with otherapproaches [1, 8, 9, 15]. Many researchers have extended the definitionof rough set into different backgrounds; e.g., based on neighborhoods [16],based on Galois connections [10] or based on fuzzy set theory [3]. In thiswork, we will focus on the extensions proposed in the context of fuzzy sets
and specifically, in the framework given by the multi-adjoint paradigm [5].
Such an approach has proven to be useful in different areas, such as Formal
Concept Analysis [12] or logic programming [13], among others. This fuzzy
framework provides two main advantages:• The first one is that the considered fuzzy operators do not need to becommutative nor associative, allowing to model a wider range of realsituation.
• The second one is that different operators can be considered to model
conjunctions and implications, allowing to establish different degrees
of preferences in the elements of the considered universe [4].
This multi-adjoint paradigm has already been considered in the definition of fuzzy rough sets in [5]. In this first extension of rough set, thenatural extension to the fuzzy case was considered in which the lower and?

Partially supported by the projects PID2019-108991GB-I00 and PID2022-137620NBI00/AEI/10.13039/501100011033/ FEDER, UE, and with grant TED2021-129748B-I00
funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way
of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”.
March 16, 2024

14

upper approximations were directly defined from the necessity and possibility operators, respectively. As it could also be reasonable, some properties
true in the Bolean case do not hold in this new approach. For example,
the proposed lower and upper approximation operators require extra conditions in order to guarantee that the lower approximation is contained in
the referential set and the upper approximation contains the referential set.
However, it is possible to introduce new natural definitions satisfying thislast property
In this paper, considering also the multi-adjoint property-oriented framework, and following the idea presented in [10], we propose a new definition of
the upper and lower approximation operators based on Galois connections
which satisfies the previous mentioned property, as well as other desirable
properties.
In this fuzzy generalization, we fix a multi-adjoint property-oriented
frame (P, L, L, &1 , . . . , &k ) where P is a partially ordered set, L is a complete lattice and {(&i , -i , .i ) | i ∈ {1, . . . , k}} is a family of adjoint triples
with respect to P , L and L (see [11] for more details). We also consider
an approximation space (X, R), where X is a non-empty set of objects and
R is any arbitrary P -fuzzy relation (i.e., R : X × X → P ); and a mappingτ : X × X → {1, . . . , k} used to associate each pair of objects in X with anadjoint triple in the frame.
In this environment, given an L-fuzzy set A ∈ LX , since the considered
P -fuzzy relation R may be non symmetric, we have two possibilities to defineN
the possibility and necessity operators as: ↑π : LX → LX , ↓ : LX → LX :r

A↑π (x) = sup{R(x, y) &τ (x,y) A(y) | y ∈ X}lA↑π (x) = sup{R(y, x) &τ (y,x) A(y) | y ∈ X}
N

A↓r (y) = inf{A(x) -τ (y,x) R(y, x) | x ∈ X}
NA↓l (y) = inf{A(x) -τ (x,y) R(x, y) | x ∈ X}
NlNote that, the operators previously defined satisfy that (↓r , ↑π ) andr( , ↑π ) are isotone Galois connections [11]. Hence, we propose to use thelNrNclosure and interior operators related to (↓r , ↑π ) and (↓l , ↑π ), as lower andupper approximations, as the following definition states.
↓NlDefinition 1. Given a fuzzy sets A ∈ LX , the lower approximations of A
are defined as:
N l
N r
A↓r ↑π and A↓l ↑π15and the upper approximations of A are defined as:lNA↑π ↓rr N

and A↑π ↓l .

A set A ∈ LX is called a generalized multi-adjoint fuzzy rough set if isdifferent from the two lower approximations and from the two upper approximations.
The use of interior and closure operators under the umbrella of thetheory of Galois connections imply the following properties of our proposed
upper and lower approximations:• the essential property holds, i.e., for every fuzzy set A, both lower
approximations of A are included in A, which, in turn, is included inboth upper approximations;• the upper and lower approximations are monotonic;• the upper and lower approximations are idempotent.
Moreover, due to the flexibility of the multi-adjoint paradigm, a kind ofduality is satisfied: for each upper approximation (resp. lower approximation) is possible to define, from the framework, the mapping τ and involutive
negation, a dual lower approximation (resp. upper approximation).
References[1] M. J. Benı́tez-Caballero, J. Medina, E. Ramı́rez-Poussa, and D. Ślȩzak. Rough-setdriven approach for attribute reduction in fuzzy formal concept analysis. Fuzzy Sets
and Systems, 391:117–138, 2020.
[2] F. Chacón-Gómez, M. E. Cornejo, and J. Medina. Decision making in fuzzy roughset theory. Mathematics, 11(19), 2023.
[3] M. D. Cock, C. Cornelis, and E. E. Kerre. Fuzzy rough sets: The forgotten step.
Fuzzy Systems, IEEE Transactions on, 15(1):121–130, feb. 2007.
[4] M. E. Cornejo, J. Medina, E. RamÌrez-Poussa, and C. Rubio-Manzano. Preferencesin discrete multi-adjoint formal concept analysis. Information Sciences, 650:119507,
2023.
[5] C. Cornelis, J. Medina, and N. Verbiest. Multi-adjoint fuzzy rough sets: Definition,
properties and attribute selection. International Journal of Approximate Reasoning,
55:412–426, 2014.
[6] A. Gaeta, V. Loia, and F. Orciuoli. An explainable prediction method based on
fuzzy rough sets, topsis and hexagons of opposition: Applications to the analysis of
information disorder. Information Sciences, 659:120050, 2024.

16

[7] R. Gul, M. Shabir, and M. Naeem. A comprehensive study on (α, β)-bipolar fuzzified rough set model based on bipolar fuzzy preference relation and corresponding decision-making applications. Computational and Applied Mathematics, 42:310,
2023.
[8] A. Janusz, A. Zalewska, L. Wawrowski, P. Biczyk, J. Ludziejewski, M. Sikora, and
D. Slezak. Brightbox – a rough set based technology for diagnosing mistakes ofmachine learning models. Applied Soft Computing, 141:110285, 2023.
[9] D. Lobo, V. López-Marchante, and J. Medina. Reducing fuzzy relation equations
via concept lattices. Fuzzy Sets and Systems, 463:108465, 2023.
[10] N. Madrid, J. Medina, and E. Ramı́rez-Poussa. Rough sets based on galois connections. International Journal of Applied Mathematics, 30(2):299–313, 2020.
[11] J. Medina. Multi-adjoint property-oriented and object-oriented concept lattices.
Information Sciences, 190:95–106, 2012.
[12] J. Medina, M. Ojeda-Aciego, and J. Ruiz-Calviño. Formal concept analysis via
multi-adjoint concept lattices. Fuzzy Sets and Systems, 160(2):130–144, 2009.
[13] J. Medina, M. Ojeda-Aciego, and P. Vojtáš. Multi-adjoint logic programming with
continuous semantics. Technical report, Dept. Matemática Aplicada. Univ. Málaga,
2001. Available at http://www.satd.uma.es/aciego/TR/malp-tr.pdf.
[14] Z. Pawlak. Information systems theoretical foundations. Information Systems,
6(3):205 – 218, 1981.
[15] Y. Song, H. Lin, and Z. Li. Outlier detection in a multiset-valued information
system based on rough set theory and granular computing. Information Sciences,657:119950, 2024.
[16] Y. Yao. Relational interpretations of neighborhood operators and rough set approximation operators. Information Sciences, 111(1):239 – 259, 1998.

17

Aspect-Based Sentiment Analysis in the LLM Era

Nikita Neveditsin1[0009−0000−9859−3905] , Pawan Lingras1[0000−0002−2149−7242] ,and Vijay Mago2[0000−0002−9741−3463]
1Saint Mary’s University, Halifax NS B3H 3C3, Canada
2York University, Toronto ON M3J 1P3, Canada
nikita.neveditsin@smu.ca
pawan.lingras@smu.ca
vmago@yorku.ca

Abstract. In this extended abstract, we present the outcomes of ourpilot experiment, which concentrates on the in-context learning (ICL)
abilities of Large Language Models (LLMs) for the Aspect-Based Sentiment Analysis (ABSA) task. This task utilizes a novel dataset designed
to reduce the memorization effect. Our experiments indicate that LLMs
are capable of performing comparably to one of the current state-ofthe-art models without the need for additional fine-tuning or adaptation. Moreover, the experiment highlights the inadequacies of traditionalclassification metrics in evaluating models on ABSA tasks and suggests
further research directions in this domain.
Keywords: LLM · Sentimend Analysis · NLP · Aspect-Based Sentiment
Analysis · Metrics

1

Introduction

Large Language Models have ushered in a transformative era in the field of
Natural Language Processing (NLP), reshaping the landscape of classic NLP
tasks, including aspect-based sentiment analysis. The rapid development anddeployment of LLMs, such as the Generative Pre-trained Transformer (GPT)
series, Bidirectional Encoder Representations from Transformers (BERT), andtheir successors, have enhanced the ability of machines to understand, generate,and interpret human language with unprecedented accuracy and nuance.
Historically, ABSA has posed a significant challenge within the field of NLP,
necessitating the identification of specific aspects mentioned in the text anddetermining the sentiment polarity expressed towards each of those aspects.
Traditional approaches often depended on manually crafted features, rule-based
systems, or machine learning models which, although somewhat effective, wereconstrained by their limited capacity to fully comprehend the complexities andsubtleties of language. The advent of LLMs has shifted this paradigm, offering a
new approach that enhances the ability to analyze sentiment at a more granularlevel [2].
18This study endeavors to examine the role of LLMs in transforming ABSA,
discussing the methodologies that utilize these models, the challenges faced in
adapting them to ABSA tasks, and the opportunities they offer for future research and applications.

2

Aspect-Based Sentiment Analysis

Aspect-based Sentiment Analysis is a nuanced form of sentiment analysis that
focuses on identifying the sentiment toward specific aspects within a text [3].
For instance, in the sentence “The soup was delicious”, the aspect term (AT)“soup” refers to the specific item being evaluated, while the aspect category(AC) could be “food”, and the opinion term (OT) is “delicious”. Generally,
ABSA task for identifying sentiments towards specific aspects mentioned in atext can be formulated as follows. Given a text document d, the task involves
two main steps: aspect extraction and sentiment classification. First, identify aset of aspects Ad = {a1 , a2 , . . . , aM } mentioned in d by applying a function:
Fa : D → P (A)(1)

that maps a document d ∈ D to a set of aspects Ad ⊆ A, where D is the domainof all documents and A is the set of all possible aspects.
Second, for each aspect ai ∈ Ad , we determine the sentiment polarity pai ∈ P
expressed towards it in d ∈ D. We can express it as a function
Fs : A × D → P(2)that assigns a sentiment polarity to an aspect a from A identified in a document d from D, where P represents the set of possible sentiment polarities, e.g.,{positive, neutral, negative}.
The overall objective of ABSA can be formulated as a joint task of extracting aspects and classifying polarities towards these aspects. In a case of supervised learning, given a dataset D = {(di , {(aij , pij ) | j = 1, 2, . . . , ni }) | i =1, 2, . . . , N }, where dn ∈ D is a document, aij ∈ Ad an aspect in dn , and pij ∈ P
a polarity towards aij , the goal is to accurately learn the two functions Fa andFs from the given data. Various studies extend the ABSA task to include opinion
extraction [4] and attribute labeling [3]; however, we consider these subtasks to
be auxiliary and thus do not cover them in our study.
Our partner organization supplied us with survey data from a sporting event,which included 630 documents containing feedback from volunteers. For our
experiment, we selected a sample of 40 documents and manually annotated themfor the Aspect-Based Sentiment Analysis (ABSA) task by identifying aspects andtheir corresponding polarities. As a baseline model, we utilized the DeBERTa-v2based PyABSA framework [1] [5]. To assess the performance of Large Language
Models (LLMs) on the ABSA task, we employed three models: ChatGPT-3.5,
ChatGPT-4, and LLaAMA-2-7b-chat.
19

3Preliminary Results

The results for the aspect detection subtask reveal that GPT-3.5 surpasses othermodels in recall (Rabsa = 0.70) and F1-score (F 1absa = 0.53), suggesting its
superior ability to align with human judgments regarding aspects within thenovel dataset. In contrast, PyABSA (DeBERTa-v2), GPT-4, and LLaMA-2-7b
demonstrate a more balanced, yet overall lower, performance. Notably, the performance of GPT-4 (Pabsa = 0.28, Rabsa = 0.48, F 1absa = 0.35) is particularlyinteresting, considering its architectural improvements and the extensive size of
its training corpus. A closer examination of the results indicates that GPT-4
tends to produce longer, albeit valid, descriptions of aspects. This observation
underscores the limitations of our evaluation metric in capturing the semantic
similarity between aspects.
In the polarity classification subtask, GPT-4 stands out with the highest
precision (0.84), recall (0.85), and F1-score (0.84), demonstrating its proficiency
in discerning and categorizing sentiment once aspects are identified. Meanwhile,
PyABSA (DeBERTa-v2) and LLaMA-2-7b show moderate performance, with
LLaMA-2-7b experiencing a notable decline in recall (0.51), which may indicate challenges in consistently recognizing the correct sentiment for all aspects.
This could point to constraints in model pre-training or an intrinsic difficulty indifferentiating subtle sentiment nuances within the dataset.
Overall, this pilot experiment underscores the feasibility of employing unmodified LLMs in ABSA, highlighting their potential to excel in ABSA tasks
without the need for specific fine-tuning or adaptation.

4

Future Work and Conclusions

This pilot study critically examines the use of state-of-the-art LLMs including
GPT-3.5, GPT-4, and LLaMA-2-7b, in the context of aspect detection and polarity classification, evaluated against a novel dataset. It contributes to the fieldof natural language processing by underscoring the strengths and weaknessesof these models in tasks that require a nuanced understanding of text without
necessitating additional training. Furthermore, it underscores the necessity fora more sophisticated metric to evaluate the aspect detection task—one that canalign sets of predicted aspects based on their semantic similarity.
The incorporation of a novel dataset is crucial in reducing the memorization
effect commonly observed in models trained on frequently utilized benchmarks.
By testing the models with previously unseen data, the study sheds light on
their authentic understanding and generalization capabilities, thereby providing
a more accurate measure of their suitability for real-world applications.
The study highlights the importance of continued exploration in ABSA for
our next iteration, focusing on several critical areas. Initially, we aim to improve
aspect matching accuracy by integrating semantic similarity measures into the
aspect detection process. Utilizing methods like word embeddings, contextual
embeddings from transformer-based models, or a combination of lexical and20semantic similarities could enable the system to recognize and match aspects
that are semantically related but not lexically identical. This enhancement is
expected to refine the accuracy of aspect detection and polarity classification.
Furthermore, we will explore the impact of model fine-tuning or adaptation
for ABSA tasks on public datasets to assess how these adjustments might affectperformance on new datasets. This investigation stands as another crucial area
for our forthcoming research efforts.
Extending the analysis to include the identification of emotions towards aspects offers a promising yet challenging research direction. For instance, thesentence “The sight of the cherry blossoms reminded her of the beauty in theworld” would associate the aspect term “cherry blossoms” with the emotion“Joy”. The main challenge here is compiling and annotating a comprehensive
dataset that covers a wide spectrum of emotions, potentially adapting this task
to the unsupervised learning domain. Such an approach could be particularly
beneficial for developing human-centered AI applications in various fields.
Looking forward, future research should explore leveraging and improving incontext learning capabilities of LLMs within ABSA, and investigate knowledge
distillation techniques to enhance computational efficiency. By focusing on theseareas, researchers can further unlock the potential of LLMs in ABSA, allowing
for more accurate, flexible, and efficient model performances.

References
1. He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention (2021)
2. Kheiri, K., Karimi, H.: Sentimentgpt: Exploiting gpt for advanced sentiment analysis
and its departure from current machine learning. arXiv preprint arXiv:2307.10234(2023)
3. Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., ALSmadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016
task 5: Aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation(SemEval-2016). pp. 19–30. Association for Computational Linguistics (2016)
4. Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction
learning for aspect based sentiment analysis. arXiv preprint arXiv:2302.08624 (2023)
5. Yang, H., Zhang, C., Li, K.: Pyabsa: A modularized framework for reproducible aspect-based sentiment analysis. In: Proceedings of the 32nd
ACM International Conference on Information and Knowledge Management. p. 5117–5122. CIKM ’23, Association for Computing Machinery,
New York, NY, USA (2023). https://doi.org/10.1145/3583780.3614752,
https://doi.org/10.1145/3583780.3614752

21

Interactive Machine Learning andData Labeling – Can Rough Sets Help?
Dominik Ślęzak1,2[0000−0003−2453−4974]
2

1
QED Software, Poland
Institute of Informatics, University of Warsaw, Poland

Abstract. We discuss the Label-In-The-Loop (LITL) software solution
which is aimed at interactive data labeling. We consider some of possible rough set extensions of LITL. We focus on the aspects of machinelearning (ML), data selection, uncertainty modeling, different scenariosof labeling, as well as making the outputs of LITL more insightful.
Keywords: Interactive ML · Data Labeling · LITL · Rough Sets.

1

Introduction

A machine learning (ML) process needs to explore a huge space of possiblemodels to find the one which fits the available data and assures the highest
chance for good performance after deploying it within a production environment.
The complexity of the space of possible models can be determined by the rangesof parameters of a selected ML algorithm and a variety of information sources
and attributes that can be used by the algorithm. On top of that, to learn agood model, one needs the data of a sufficiently good quality. In particular, thedata needs to be representative and correctly labeled, at least partially.
Given the above kind of complexity, it may be useful to design some humancomputer interaction mechanisms so subject matter experts (SMEs) can help
(if they want and know how) the ML process to focus on the most promisingmodel subspaces and the most valuable data cases. For example, if there is achance to get SMEs involved into the phase of feature selection, then they canhelp the algorithm to omit attributes that they would not like to see in a later
decision process anyway. (This way the algorithm can sooner concentrate ona smaller subspace of features, its work becomes faster, and its results become
more acceptable for SMEs.) As another example, if there is a chance to get SMEs
involved into the phase of data labeling, then they can help the algorithm byfeeding it with information required for the model training. (This is particularly
important if the data is unlabeled and its labeling requires high expertise. In
such cases the mechanism of selecting the most valuable data cases to be labeled
is crucial because the time of SMEs is limited and expensive.)
In this report, we consider an example of an interactive ML technology, called
Label-In-The-Loop (LITL) [8, 9], which aims at semi-automatic data labeling.
We discuss its current state of development and elaborate on its further roadmap,
with a special emphasis on its rough-set-related extensions [13, 15].
22

2

The LITL Technology

LITL has been developed by QED Software to support commercial ML projects
in which data sets available for training are not labeled or are labeled insufficiently. The conceptual design of LITL follows the principles of active learning[12, 5]. From a software architecture viewpoint, besides monitoring, administration and other typical functions, LITL is composed of the following layers:– Data processing. This part supports continuous updates to a data store(arrivals of new data cases, recording labels added by SMEs), as well as maintaining data cases in both “SME-friendly” and a transformed “ML-friendly”
form (so they are ready to show them to SMEs and feed ML algorithms withthem). This part also collects metadata which may be useful while producing
the final LITL outputs (the attribute importance rankings, the quality anduncertainty scores of ML models and so on) and monitoring whether it works
correctly (e.g. the history of human labelings and ML model inferences).
– Data displaying. This part embraces “widgets” adjusted to visualize different data types, such as texts, images, logs, time series and so on. Thanks
to that, based on information about the underlying original “SME-friendly”
data schema, data cases can be shown effectively. Data cases can be multimodal, e.g., tuples consisting of images and standard attributes. This partalso takes care of different display modes, e.g., case by case, in batches(wherein one can label each case independently or sort cases due to theirlikelihood to be assigned with a specific label) and so on.
– Data selection. Based on the previous data selection phases, as well ason the performance of an ML model learnt up to now on both labeled andunlabeled data, this part needs to choose the next cases to be examined by
SMEs. In principle, LITL takes into account ML models’ uncertainty about
particular cases, similarity of those cases to the previously labeled cases, aswell as potential mistakes in the human labeling processes.
– Machine learning. This part is responsible for continuous training andapplying ML models for the purposes of uncertainty estimation and SMEs’
verification. We used to assume that the users of LITL might wish to upload
their own ML methods for training. However, a general business requirement
is rather that LITL trains reasonable ML models by itself. The final outcomeof LITL is a set of labeled data. However, the users may also request a final
serialized ML model or at least some insights about attributes which can beimportant for further learning stages. On the other hand, in some scenarios
the users may wish LITL to become their everyday inference engine.
From a research perspective, our efforts up to now concentrated on designing
the aforementioned ML model uncertainty measures and applying them to thephase of data selection [8], as well as modeling and resolving inconsistencies
among SMEs’ labelings [9]. In the next section, we discuss some ideas how toutilize rough set algorithms and paradigms in the overall LITL process.
23

3

How Rough Sets Can Help?

There are several opportunities of strengthening LITL with rough sets:– Rough sets for ML. One can rely on reducts and their ensembles as MLmodels. Particularly the ensembles of approximate reducts and bireducts canbe promising [6, 13]. Moreover, one can adapt some methods for incremental reduct calculations [3], which seems to be useful from the viewpoint ofcontinuous re-learning of ML models within the active learning loop.
– Rough sets for data selection. There are a number of rough set algorithms for selecting not only attributes but also instances [2]. Moreover, oneof data selection principles used in LITL is to avoid showing too similarcases to SMEs. Therefore, some data clustering techniques can be applied,in particular rough clustering [11]. Interestingly, rough sets can be also used
to define and calculate similarities. Actually, in LITL we used to consider
some advanced similarity structures that would let us quickly generate representative data batches for labeling [17]. However, somewhat analogously
to Locality Sensitive Hashing (LSH) [16], it is also possible to define a similarity measure by means of counting how many common decision rules are
triggered for the given two data cases within a precomputed ensemble of decision reducts [7]. To keep the things simple, one can use the same ensembleof reducts as an ML model and as the basis for similarity calculations.
– Rough sets for uncertainty. ML model uncertainties for the given datacases are usually expressed based on the analysis of label distributions observed for their most similar cases. Then one can apply, e.g., the DempsterShafer’s theory of evidence to represent the uncertainties of ML models andhuman labelings [5, 8]. However, we also intend to consider rough set approximations and generalized decision functions for this purpose [4].
– Rough sets for data labeling. Rough-set-related paradigms can be also
used to support SMEs while labeling complex cases, whereby labelings are
sometimes too hard to decide or should be replaced by operting with preferences or rankings. The three-way decisions [18] and the dominance roughset models [15] can be applied here, respectively. Going further, instead oflabeling the whole data cases, SMEs can annotate some fragments of theircorresponding images, texts and so on. In such a scenario, rough set approximations can be useful to deal with uncertain and complex objects [1].
– Rough sets for more insights. Finally, as outlined before, we can extend
the current LITL’s functionality by delivering not only the labeled data but
also information about the most important attributes. It is worth mentioningthat rough set algorithms can produce high-quality attribute rankings [6]. It
is also possible to enrich those algorithms with some elements of interactive
feature selection [14], which can be combined within a single user interface
with interactive data labeling. Going further, we can extend LITL’s interface
by letting SMEs lead chat-based discussions about the hardest data cases
and use some NLP methods [10] to analyze those discussions to search for
attributes that seem to be most important in SMEs’ decision making.
24

Acknowledgments. This research was co-funded by Smart Growth Operational Programme 2014-2020, financed by European Regional Development Fund, in frame of
project POIR.01.01.01-00-0213/19, operated by National Centre for Research and Development in Poland.

References
1. Beaubouef, T., Petry, F.E., Ladner, R.: Spatial Data Methods and Vague Regions:
A Rough Set Approach. Applied Soft Computing 7(1), 425–440 (2007)
2. Cornelis, C.: Hybridization of Fuzzy Sets and Rough Sets: Achievements and Opportunities. In: Proc. of FedCSIS 2022. pp. 7–14 (2022)
3. Dong, L., Chen, D.: Incremental Attribute Reduction with Rough Set for Dynamic
Datasets with Simultaneously Increasing Samples and Attributes. International
Journal of Machine Learning and Cybernetics 11(6), 1339–1355 (2020)
4. Dutta, S., Ślęzak, D.: Nature of Decision Valuations in Elimination of Redundant
Attributes. International Journal of Approximate Reasoning 165, 109091 (2024)
5. Hoarau, A., Martin, A., Dubois, J., Gall, Y.L.: Imperfect Labels with Belief Functions for Active Learning. In: Proc. of BELIEF 2022. pp. 44–53 (2022)
6. Janusz, A., Ślęzak, D., Stawicki, S., Stencel, K.: A Practical Study of Methods
for Deriving Insightful Attribute Importance Rankings Using Decision Bireducts.
Information Sciences 645, 119354 (2023)
7. Janusz, A., Zalewska, A., Wawrowski, Ł., Biczyk, P., Ludziejewski, J., Sikora, M.,Ślęzak, D.: BrightBox – A Rough Set Based Technology for Diagnosing Mistakesof Machine Learning Models. Applied Soft Computing 141, 110285 (2023)
8. Kałuża, D., Janusz, A., Ślęzak, D.: On Several New Dempster-Shafer-Inspired Uncertainty Measures Applicable for Active Learning. In: Proc. of IJCRS 2023. pp.
479–494 (2023)
9. Kałuża, D., Janusz, A., Ślęzak, D.: Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations. In: Proc. of ECAI 2023. pp. 1207–1214(2023)
10. Laskar, M.T.R., Bari, M.S., Rahman, M., Bhuiyan, M.A.H., Joty, S., Huang, J.X.:
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark
Datasets. In: Proc. of ACL 2023. pp. 431–469 (2023)
11. Lingras, P., Peters, G.: Rough Clustering. WIREs Data Mining and Knowledge
Discovery 1(1), 64–72 (2011)12. Settles, B.: Active Learning. Morgan & Claypool (2012)
13. Skowron, A., Ślęzak, D.: Rough Sets Turn 40: From Information Systems to Intelligent Systems. In: Proc. of FedCSIS 2022. pp. 23–34 (2022)
14. Ślęzak, D., Grzegorowski, M., Janusz, A., Stawicki, S.: Toward Interactive Attribute Selection with Infolattices – A Position Paper. In: Proc. of IJCRS 2017,
Part II. pp. 526–539 (2017)15. Słowiński, R.: Multiple Criteria Decision Aiding by Constructive Preference Learning (Keynote Lecture – Extended Abstract). In: Proc. of FedCSIS 2023. p. 39 (2023)
16. Ullman, J.: Big-Data Algorithms That Are Not Machine Learning. In: Proc. of
BigData 2023. p. 5 (2023)
17. Wu, P., Hoi, S.C.H., Nguyen, D.D., He, Y.: Randomly Projected KD-Trees withDistance Metric Learning for Image Retrieval. In: Proc. of MMM 2011, Part II.
pp. 371–382 (2011)18. Yao, Y.: An Outline of a Theory of Three-Way Decisions. In: Proc. of RSCTC2012. pp. 1–17 (2012)25

Attribute Ranking Method Based on Conditional
Comparison Matrices
Bich Khue Vo1[0000−0001−7852−796X] , Hung Son Nguyen2[0000−0002−3236−5456]
1

University of Finance and Marketing, Ho Chi Minh City, Vietnam
Email: votbkhue@gmail.com and vokhue@ufm.edu.vn
2
University of Warsaw, ul. Banacha 2, Warsaw, 02-097, Poland,
Email: son@mimuw.edu.pl

Abstract. This paper presents an extension to the rough-fuzzy hybrid
method for attribute ranking ranking problem [5], [6]. We propose a new
method for the attribute comparison with the constraint that some otherattributes must be chosen based on expert knowledge. The proposed comparison method can be applied to the attribute ranking problem in the
RAFAR framework [6], resulting in the building of a conditional intervalvalue fuzzy preference relation. The suggested approach enables the users
to impose certain attributes–designated by an expert or belonging to thekernel–at the top of the ranking list. We also propose new methods forfinding optimal rankings compatible with the comparison matrix

Keywords: Rough fuzzy hybridization · attribute ranking · Fuzzy preference
relation · optimization models

1

Introduction

In rough set theory, training data set can be presented in form of a decisiontable, which is a tuple T = (U, A ∪ {d}), where U = {u1 , · · · , um } is the set ofobjects, A = {a1 , · · · an } is the set of attributes and d is the decision attribute.
Usually, the description of objects are represented by attributes or features,
namely a1 , ..., an . Each attribute ai is in fact a function of form: ai : U → Vai ,where Vai is called the domain of ai . In another words, xi = (a1 (ui ), · · · , an (ui )).
If a is a measurement such as a person’s weight, height, blood pressure or theweather temperature, i.e. Va is a real interval, then a is called the numeric orquantitative attribute. Otherwise, if the values in Va are not comparable, or ifthey can not be ordered in a linear order, then a is called categorical, symbolicor qualitative attribute. The decision attribute is a categorical attribute and thevalues of its domain Vd are called the decision class labels.
The goal of the attribute ranking problem for decision table T = (U, A ∪ {d})is to order the feature in a ranking list so that the more important features areat the beginning of the list while the less important features are located at theend of the ranking list.
In [5], we proposed a new method for feature ranking called RAFAR (Roughfuzzy Algorithm For Attribute Ranking). This is a hybrid method that combines
26

discernibility relation of the rough set theory and the ranking method describedin the previous section. The RAFAR method consists of two main steps:(1) construction of a pairwise comparison matrix for the set of attributes and(2) searching for the optimal weight vector, which is consistent (defined later)
with the comparison matrix. This step has two options:2a) searching for real-value weight
Pn vector w = (w1 , . . . , wn ) such that wi ∈[0, 1] for i = 1, · · · , n and i=1 wi = 1,2b) searching for normalized interval-value weight vector, i.e. (see [3] and[7]) the vector w = ([l1 , r1 ], · · · , [ln , rn ]), where [li , ri ] ⊂ [0, 1] and:nX

j=1,j̸=ilj + r i ≤ 1 ≤ li +nX

j=1,j̸=irj

for each i ∈ {1, · · · , n}(1)Each weight value wi or weight interval [li , ri ] corresponds to attribute ai ∈ A
so that the higher weight means the more preferred choice.
Recall that for any attribute ak ∈ A, the discernibility relation Dak is definedas the set of pairs of objects that are discernible by ak . In particular,– if ak is a symbolic attribute, then
Dak = {(ui , uj ) ∈ U × U : d(ui ) ̸= d(uj ) and ak (ui ) ̸= ak (uj )}(2)– In the case when ak ∈ A is a continuous attribute, the domain Vak is discretized into b equal length intervals, where b is a predefined constant, andDak denotes the discernibility relation for the discretized attribute.
In [6], to compare a pair of attributes (ai , aj ) ∈ A × A, we proposed to use apair of two real values plij , prij defined by |Di |
1 − |Dj |if i ̸= j
if i ̸= jrl;
pij = |Di ∪ Dj |
(3)
pij =
|Di ∪ Dj |


0.5
0.5
if i = jif i = j.

Notice that if we consider the set ΩT = {(uk , ul ) ∈ U × U : d(uk ) ̸= d(ul )}as a probability space with uniform distribution, then we have the followinginterpretationplij = P(Di − Dj |Di ∪ Dj ): is the probability that a pair of objects is not distinguishable by aj , assuming that it is distinguishable by {ai , aj }.
prij = P(Di |Di ∪ Dj ): is the probability that a pair of objects are distinguishable
by ai , assuming that it is distinguishable by {ai , aj }.

Informally, if P (ai > aj ) denotes the probability that ai is better than aj , thenP (ai > aj ) ∈ [plij , prij ] ⊂ [0, 1]

The matrix PT = ([plij , prij ])n×n , containing pairwise comparison intervals between all pairs of attributes is called the interval-valued fuzzy preference relation(IV-FPR) over the set of attributes.
27

In [5], 4 optimization models including additive consistent models (A1),(A2) and multiplicative consistent models (M1) or (M2) were applied to generate the probability weight vector for the set of attributes. In [6], another two
optimisation models based on IV-FPR and interval-valued fuzzy vectors (IV-FV)
were presented.

2

Conditional Comparison

The limitation of the above method is the lack of taking into account expert
knowledge in the attribute ranking process. This also often happens when kernel
attributes that appear in all reducts but are not placed at the top of the ranking
list.
In this article, we propose a new method for attribute comparison in thesituation when an expert prefers certain attributes or a user requires that kernel
attributes be at the beginning of the ranking list.
Firstly, let’s extend the discernibility relation defined in Eq. 2. Let B ⊂ A
be an arbitrary subset of attributes. A pair of objects (uk , ul ) ∈ U × U is called
distinguishable by B if d(uk ) ̸= d(ul ) and there exists an attribute a ∈ B suchthat a(uk ) ̸= a(ul ). The set of all pairs of objects distinguishable by B ⊂ A isdenoted by
DB = {(uk , ul ) ∈ U × U : d(uk ) ̸= d(ul ) and (uk , ul ) is distinguishable by B}.
DB is the discernibility relation, which is the building block in rough set theory[1].
In this paper, we propose a generalization of Eq. 3. For any B ⊂ A, we
consider the set ΩT − DB , i.e. the pairs that are not discernible by B. We defineplij (B) and prij (B) as the probabilities that a pair of objects is not distinguishable
by aj (distinguishable by ai ), assuming that it is distinguishable by {ai , aj } but
not by B. Formally, plii (B) = prii (B) = 0.5 and if i ̸= j thenplij (B) =prij (B) =|DB∪{ai ,aj } | − |DB∪{aj } ||Di − Dj − DB |=|Di ∪ Dj − DB ||DB∪{ai ,aj } | − |DB ||Di − DB ||DB∪{ai } | − |DB |=|Di ∪ Dj − DB | |DB∪{ai ,aj } | − |DB |Obviously, [plij (B), prij (B)] evaluates which of the attributes ai , aj is a better
compliment to B, i.e.
P (ai > aj |B) ∈ [plij (B), prij (B)] ⊂ [0, 1]
The conditional comparison matrix PT (B) = ([plij (B), prij (B)]) is also an IVFPR and is called B-conditional IV-FPR. It is obvious that PT = PT (∅).
Let us illustrate our approach by the following example:
28

Table 1. A sample decision table with 5 attributes and 10 objects
T1 a1
a2
a3
a4
a5
dec
ID outlook temp. hum. windy smog play
1 sunny hot high
FALSE nono
2 sunny hot high
TRUE nono
3 overcast hot high
FALSE yes yes
4 rainy
mild high
FALSE yes yes
5 rainy
cool normal FALSE noyes
6 rainy
cool normal TRUE nono
7 overcast cool normal TRUE noyes
8 sunny mild high
FALSE nono
9 sunny cool normal FALSE noyes
10 rainy
mild normal FALSE no
yes

Example 1. Table 1 provides an example of a small decision table, T1 . The firstten objects from the well-known "weather data set" were used to produce thistable, and a new attribute smog was included as the fifth attribute.
The discernibility relations for attributes of T1 are presented in Table 2. One
can check that this decision table has exactly 2 reducts: R1 = {a1 , a2 , a4 } andR2 = {a1 , a3 , a4 }. According to [2], the features a1 and a4 are called the core
attributes, and a5 is the redundant attribute.
Now we can calculate the IV-FPR for this table. Let us illustrate our approach
for a few pairs of attributes:

6 18(a1 , a2 ) : |D1 | = 18; |D2 | = 17; |Da1 ,a2 | = 23=⇒ [pl1,2 , pr1,2 ] =
,
23 23


12
17
r
l
(a2 , a5 ) : |D2 | = 17; |D5 | = 8; |Da2 ,a5 | = 20=⇒ [p2,5 , p2,5 ] =
,
20 20


7 14
(a3 , a4 ) : |D3 | = 14; |D4 | = 12; |D{a3 ,a4 } | = 19 =⇒ [pl3,4 , pr3,4 ] =,19 19Now, let compare the attributes a3 and a4 assuming that an expert wishes toplace the set B = {a2 } at the begin of the final ranking:|D2 | = 17;|D{a2 ,a3 } | = 18; |D{a2 ,a4 } | = 20; |D{a2 ,a3 ,a4 } | = 21


1 1
l
r
,
=⇒ [p3,4 (a2 ), p3,4 (a2 )] =4 4This example presents the fact that, even a3 is slightly preferable to a4 , but
probably a3 is a worse compliment to B = {a2 } than a4 .

3

Searching for Consistent Weight Vectors

In this paper we also propose some new methods for searching for the optimal
normalized weight vectors that are consistent with a given pairwise comparison
29

Table 2. The discernibility relations Di = Dai of attributes in decision table T1 .
... D1 D2 D3 D4 D5(7,1) 1 1 1 1 0(7,2) 1 1 1 0 0(7,6) 1 0 0 0 0(7,8) 1 1 1 1 0(9,1) 0 1 1 0 0(9,2) 0 1 1 1 0(9,6) 1 0 0 1 0(9,8) 0 1 1 0 0(10,1) 1 1 1 0 0(10,2) 1 1 1 1 0(10,6) 0 1 0 1 0(10,8) 1 0 1 0 0ΩT1 D1 D2 D3 D4 D5(3,1) 1 0 0 0 1(3,2) 1 0 0 1 1(3,6) 1 1 1 1 1(3,8) 1 1 0 0 1(4,1) 1 1 0 0 1(4,2) 1 1 0 1 1(4,6) 0 1 1 1 1(4,8) 1 0 0 0 1(5,1) 1 1 1 0 0(5,2) 1 1 1 1 0(5,6) 0 0 0 1 0(5,8) 1 1 1 0 0

matrix (generated by the presented above methods). As an example, the IV-FPR
for T1 is as follows:[0.50, 0.50] [0.26, 0.78] [0.36, 0.82] [0.45, 0.82] [0.58, 0.95]
[0.22, 0.74] [0.50, 0.50] [0.22, 0.94] [0.40, 0.85] [0.60, 0.85]
PT 1 = [0.18, 0.64] [0.06, 0.78] [0.50, 0.50] [0.37, 0.74] [0.60, 0.70]
[0.18, 0.55] [0.15, 0.60] [0.26, 0.63] [0.50, 0.50] [0.50, 0.75]
[0.05, 0.42] [0.15, 0.40] [0.30, 0.40] [0.25, 0.50] [0.50, 0.50]
The first method is converting the IV-FPR PT = ([plij , prij ])n×n into a Fuzzy
Preference Relation RT = (pij )n×n , wherepij =plij + prij
2

Recall that the fuzzy matrix RT = (pij )n×n is called the Fuzzy Preference
Relation (FPR) if pij + pji = 1 for all i, j ∈ {1, ..., n}. In our example:

0.5 0.52 0.59 0.64 0.77
0.48 0.5 0.58 0.63 0.73
RT 1 = 0.41 0.42 0.5 0.56 0.65
0.36 0.37 0.44 0.5 0.63
0.23 0.27 0.35 0.37 0.5

According to [8], a given fuzzy preference relation P = (pij )n×n is called
1. a-consistent if and only if there exists a normalized vector w = (w1 , · · · , wn ),w −w +1
such that pij = i 2 j ;2. P is m-consistent if and only if there exists a normalized vector w = (w1 , · · · , wn ),isuch that pij = wiw+w;
j30

In other words, for any normalized weight vector w = (w1 , · · · , wn ), one canconstruct 2 fuzzy preference relations: A(w) = [aij ] and M(w) = [mij ], whereaij =1(1 + wi − wj )
2

and

mij =

wi
wi + w j

Most of the existing a-consistent models are minimizing the norm ∥P − A(w)∥1 ,see [9], [4]. In this paper we propose a ranking method by minimization of thefollowing functions
2n 
Xwi − wj + 1
A(w1 , · · · , wn ) = ∥P − A(w)∥2F =
− pij
(4)
2
i,j=1
2
n 
X
wi
2
M(w1 , · · · , wn ) = ∥P − M(w)∥F =
− pij
(5)
wi + wji,j=1
The first function is in fact the square of the Frobenius norm of P − A(w) or
the sum of square errors between P and A(w). The second function is the sumof square errors between P and M(w).
Fortunately, the minimum of function A(w1 , · · · , wn ) is unique and can befound by analytical method. Indeed,nX∂A= n · wk − 1 + n − 2
pkj
∂wk
j=1

∂A
2Pk + 1= 0 ⇔ wk =
− 1,
∂wk
n

where Pk is the sum of values in the k th row of the matrix P. The function
M(w1 , · · · , wn ) is more complex and its minimum can be found using gradientdescent technique.
This approach can be applied for different types of preference matrices (FPR
or IV-FPR), different types of consistency (a-consistency or m-consistency) as
well as different types of weight vectors (real-value or interval-value). The otherresults will be presented in the full version of our paper.

4

Conclusions

In this version (extended abstract) we briefly presented some new rough-fuzzy
hybrid methods for the attribute ranking problem. The complete result and theexperiment results will be presented in the full version of our peper.

References
1. Nguyen, H.S.: Approximate Boolean Reasoning: Foundations and Applications in
Data Mining, p. 334–506. Springer-Verlag, Berlin, Heidelberg (2006)31

2. Nguyen, L.G., Nguyen, H.S.: On elimination of redundant attributes from decisiontable. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference
on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland,
9-12 September 2012, Proceedings. pp. 317–322 (2012)
3. Sugihara, K., Ishii, H., Tanaka, H.: Interval priorities in ahp by interval regression
analysis. European Journal of Operational Research 158, 745–754 (02 2004)
4. Torun, H.: Group decision making with intuitionistic fuzzy preference relations.
Knowledge-Based Systems 70 (04 2014)
5. Vo, B.K., Nguyen, H.S.: Feature selection and ranking method based on intuitionistic
fuzzy matrix and rough sets. In: Proceedings of the 17th Conference on Computer
Science and Intelligence Systems. Annals of Computer Science and Information
Systems, vol. 30, pp. 279–288 (2022)
6. Vo, B.K., Nguyen, H.S.: Application of interval valued fuzzy sets in attribute ranking. In: Huynh, V.N., Le, B., Honda, K., Inuiguchi, M., Kohda, Y. (eds.) Integrated
Uncertainty in Knowledge Modelling and Decision Making. Lecture Notes in Computer Science, vol. 14375, pp. 61–72. Springer Nature Switzerland, Cham (2023)
7. Wang, Z.J., Li, K.W.: Goal programming approaches to deriving interval weights
based on interval fuzzy preference relations. Information Sciences 193, 180–198(2012)
8. Xu, Z.: A survey of preference relations. International Journal of General Systems
36(2), 179–203 (2007)
9. Xu, Z., Yager, R.R.: Intuitionistic and interval-valued intutionistic fuzzy preference
relations and their measures of similarity for the evaluation of agreement within agroup. Fuzzy Optimization and Decision Making 8, 123–139 (2009)32

Cross-domain Structural Damage Identification based on
Deep Domain Adaptation Algorithm
Hao Wang 1,2, Baoli Wang 1, Caixia Cui 2, Pengling Wang 1
1 Yuncheng University, Yuncheng, 044000, China

2 Taiyuan Normal University, Jinzhong, 030619, China

pollycomputer@163.com

Abstract. Against the backdrop of rapid socio-economic progress and the continuous increase in traffic volume, bridges play a crucial role as essential components of transportation hubs, ensuring road traffic safety. However, with the aging of bridges and dynamic changes in the surrounding environment, the safetyof bridge structures has become a prominent concern. Existing methods forbridge damage detection have not thoroughly explored the monitoring data collected by sensors. Moreover, due to the dynamic changes in the loads borne bybridges, existing deep learning methods often suffer from insufficient generalization performance, failing to meet the real-time and high-precision requirementsof bridge monitoring. Therefore, researching and developing data-driven methods for bridge structural damage identification, prioritizing real-time performance and high accuracy, becomes particularly important.
Considering the shortcomings of existing deep learning-based methods forbridge structural damage identification, we have undertaken the following research efforts:
1. Construction of a bridge damage dataset with dynamic load variations. Dueto considerations of cost-effectiveness and safety, there is a scarcity of datasets
related to bridge structural damage that meet the experimental exploration requirements. In light of this, the present study utilizes ANSYS simulation software
to construct four distinct bridge damage scenarios. For each scenario, transient
analyses are conducted under various load conditions to acquire the acceleration
responses of the bridge structure as an evaluation metric for bridge damage.
These scenarios include the passage of a single vehicle (car1), two vehicles(car2), and three vehicles (car3).
2. Multi-Channel Data Fusion. Existing deep learning methods often only consider dividing data obtained from individual sensors into windows as singlechannel samples to train models. However, they neglect the correlation between
various sensors within the entire sensor system, leading to unnecessary waste ofcomputational resources. In order to fully exploit data information while taking
into account the inter-sensor correlations, this study performs data-level fusion
on multiple data streams acquired by the sensor system under the same scenario.
This approach constructs multi-channel samples for model training.
3. Integration of attention mechanism in multi-scale parallel Convolutional
Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) feature
extractors. We propose an algorithm incorporating an attention mechanism into33

a multi-scale parallel CNN and BiGRU as a damage feature extractor. Firstly, themulti-scale CNN is employed to extract spatial feature information at different
granularities from the input data. Concurrently, the parallel BiGRU, operating
alongside the CNN, is utilized to capture temporal feature information from theacceleration responses, mitigating the issue of information loss caused by the serial nature of CNN processing. Finally, the addition of the attention mechanism
allows the neural network model to focus more on the mutation signals that represent damage, capturing more valuable damage features and enhancing the data's
representational capacity.
4. Cross-Domain Structural Damage Identification. Considering the dynamic
variations in load moments on bridge structures, the Mean Maximum Discrepancy (MMD) is employed as a domain loss evaluation metric to construct a domain adaptation algorithm. This algorithm aims to extract domain-invariant features from the structure, thereby enhancing the model's generalization performance. The model utilizes a multi-scale parallel CNN and BiGRU network structure, incorporating an attention mechanism as a feature extractor. It performs feature extraction separately on source domain samples and a limited amount of target domain samples. Subsequently, a domain adaptation algorithm is employed
for feature alignment. Through iterative backpropagation, the model continuously optimizes parameters, yielding the best-trained model for cross-domain
structural damage identification.
To validate the model's generalization and robustness, this study defined six
different cross-domain damage scenarios, including car1→car2, car1→car3,
car2→car3, car3→car1, car3→car2, and car2→car1. The model achieved accuracy rates of 93.08%, 91.15%, 92.31%, 95.38%, 97.69%, and 98.46%, respectively, in the six different cross-domain damage recognition scenarios.
Keywords: Deep Learning; Transfer Learning; Attention Mechanism; Structural Health Monitoring; Domain Adaption.

34

Sequential Three-way Detection Method ofHybrid Concept Drift
Ruihua Wang1 , Jianfeng Xu1,2 , and Hong Rao1
School of Software, Nanchang University, Nanchang, 330047, Jiangxi, China
artak01x@gmail.com jianfeng_x@ncu.edu.cn raohong@ncu.edu.cn
2Nanchang Kindly(KDL) Medical Technology Co., Ltd, Nanchang, 330000, Jiangxi,
China
1

Keywords: Streaming Data · Concept Drift · Detection.
Concept drift refers to the phenomenon that the distribution characteristics ofdata may change over time in a streaming data environment [2], which has
negative influence on the model decision accuracy [1, 5]. Most research mainly
focus on the single type of concept drift currently [4], while little literature study
the hybrid concept drift [7].
In practical application scenarios, concept drift is often uncertain, whichmeans that the change of data distribution may occur suddenly or may be a
gradual process [6,8]. Some scholars divide concept drift into two types according
to the speed at which it occurs: abrupt drift and gradual drift [9]. Abrupt or
rapid changes between different concepts are referred to as abrupt drift, while
relatively slow changes between different concepts are referred to as gradual drift.
This work introduces three-way decisions [10], one of the important results in
granular computing [3], and proposes a sequential three-way detection methodof hybrid concept drift (S3-HDD).
Suppose that there is a stream data set SD = {S0 , S1 , S2 , · · · Sn } with atime series relationship in stream computing, and any two adjacent elements
in SD have the same time interval. Most elements Sx ∈ SD in the streaming data SD = {S0 , S1 , S2 , · · · Si } obey the same distribution characteristics
and satisfy the same conceptual model f . In order to detect the concept driftof the subsequent data flowing into SD starting from the current latest datanode Si . We first define two forward detection windows, namely the short forward detection window F Dw1 = {Si+1 , · · · Si+w1 } and the long forward drift
detection window F Dw2 = {Si+1 , · · · Si+w2 }, whose lengths are w1 and w2
respectively and w1 < w2 ; secondly, we define a backward detection window
BDw = {Si−w+1 , · · · Si−1 , Si } ,which has a fixed length w.
Based on the above definition, the initial conceptual model f is used to test
the data in the backward detection window BDw and the forward detection windows F Dw1 and F Dw2 . The prediction accuracy obtained respectively can be
defined as the backward detection window accuracy ACCBDw , short forward detection window accuracy ACCF Dw1 and long forward detection window accuracy
ACCF Dw2 .
When there is a streaming data SD = {S0 , S1 , S2 , · · · Si } that obeys the same
distribution characteristics, two forward detection windows F Dw1 , F Dw2 and a35

backward detection window BDw are set from the latest data node Si . Thenthe corresponding value ratio between ACCF Dw1 and ACCBDw can be defined
as a short-time drift measurement metric, recorded as DMSt (Short-time drift
metric); the value ratio between ACCF Dw2 and ACCBDw can be defined as along-time drift measurement metric, recorded as DMLt (Long-time drift metric).
The calculation formulas of the above short-time drift measurement metricand long-time drift measurement metric are as follows:
DMSt =

ACCF Dw1
× 100
ACCBDw(1)

DMLt =

ACCF Dw2
× 100
ACCBDw(2)

Based on the basic idea of three-way decisions, we set a pair of three-way
detection thresholds ⟨α, β⟩, 0 < β < α < 1; and a two-way detection thresholdγ, 0 < γ < 1. Starting from the latest data node Si , based on the values of DMSt
and DMLt and the above decision thresholds, the following sequential three-way
detection of hybrid concept drift can be performed. The sequential three-way
detection of hybrid concept drift includes two decision-making levels.
Definition 1 (The first level decision of sequential three-way detection
for concept drift).
P OS(α,β) (F Dw1 ) = {F Dw1 |DMSt ≥ α} ,BN D(α,β) (F Dw1 ) = {F Dw1 |β < DMSt < α} ,(3)N EG(α,β) (F Dw1 ) = {F Dw1 |DMSt ≤ β} .

The semantics of P OS(α,β) (F Dw1 ) in the above Equation 3 are: no concept
drift occurs in the forward detection window F Dw1 of the streaming data SD
starting from the Si node; the semantics of N EG(α,β) (F Dw1 ) are: the streaming
data SD starting from the Si node and the forward detection window STw1
occur mutation concept drift. The semantics of BN D(α,β) (F Dw1 ) are: The typeof concept drift that occurs in the data flow in F Dw1 cannot be determined.
Therefore, when F Dw1 is decided to be BN D(α,β) (F Dw1 ), in order to further
determine its drift type, it is necessary to implement the second-level decisionmaking of sequential three-way concept drift detection based on the values ofthe long forward drift detection window F Dw2 and its DMLt .
Definition 2 (The second level decision of sequential three-way detection for concept drift).
P OSγ (F Dw2 ) = {F Dw2 |DMLt ≥ γ} ,N EGγ (F Dw2 ) = {F Dw2 |DMLt < γ} .

36

(4)

The semantics of P OSγ (F Dw2 ) in the above Equation 4 are: in the streaming
data SD, a gradual concept drift occurs in the forward detection window F Dw2starting from Si ; the semantics of N EGγ (F Dw2 ) are: there is no concept drift
occurs in the forward detection window F Dw2 starting from Si .
By using the S-3WD method, the S3-HDD can hierarchically determine thetype of concept drift according to the change of model accuracy, which improves
the concept drift detection accuracy and reduces the time cost of concept drift
countermeasure calculation. Experiments show that the proposed S3-HDD algorithm can accurately determine the type of concept drift and ensure the accuracyof the concept drift adaptation algorithm, and reduce the time cost of concept
drift countermeasure calculation.
Acknowledgments. This work is supported by National Natural Science Foundationsof China (Grant No.62266032), and Jiangxi Training Program for Academic and Technical Leaders in Major Disciplines Leading Talents Project (Grant No.20225BCI22016).
Disclosure of Interests. The authors declare that they have no known competing
financial interests or personal relationships that could have appeared to influence thework reported in this paper.

References
1. Abbasi, A., Javed, A.R., Chakraborty, C., Nebhen, J., Zehra, W., Jalil, Z.: Elstream: An ensemble learning approach for concept drift detection in dynamic
social big data stream learning. IEEE Access 9, 66408–66419 (2021)
2. Agrahari, S., Singh, A.K.: Concept drift detection in data stream mining: A literature review. Journal of King Saud University-Computer and Information Sciences
34(10), 9523–9540 (2022)
3. Bargiela, A., Pedrycz, W.: Granular computing. In: Handbook on Computer Learning and Intelligence: Volume 2: Deep Learning, Intelligent Control and Evolutionary Computation, pp. 97–132. World Scientific (2022)
4. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection.
In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on
Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004.
Proceedings 17. pp. 286–295. Springer (2004)
5. Guo, H., Li, H., Ren, Q., Wang, W.: Concept drift type identification based on
multi-sliding windows. Information Sciences 585, 1–23 (2022)
6. Liu, W., Zhang, H., Ding, Z., Liu, Q., Zhu, C.: A comprehensive active learning
method for multiclass imbalanced data streams with concept drift. KnowledgeBased Systems 215, 106778 (2021)
7. Maaradji, A., Dumas, M., La Rosa, M., Ostovar, A.: Fast and accurate businessprocess drift detection. In: Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31–September 3, 2015, Proceedings
13. pp. 406–422. Springer (2015)
8. Mohawesh, R., Tran, S., Ollington, R., Xu, S.: Analysis of concept drift in fakereviews detection. Expert Systems with Applications 169, 114318 (2021)
9. Yang, L., Shami, A.: A lightweight concept drift detection and adaptation framework for iot data streams. IEEE Internet of Things Magazine 4(2), 96–101 (2021)
10. Yao, Y.: The geometry of three-way decision. Applied Intelligence 51(9), 6298–6325(2021)

37

Representing Pedagogic Content Knowledge
Through Rough Sets
A Mani⋆
Machine Intelligence Unit, Indian Statistical Institute, Kolkata
203, B. T. Road, Kolkata-700108, India
Email: a.mani.cms@gmail.com amani.rough@isical.ac.in
Homepage: https://www.logicamani.in
Orcid: https://orcid.org/0000-0002-0880-1035

Concepts of knowledge can be associated with granular and nongranularsemantics of rough sets from multiple perspectives such as those based onclassical rough ideas, mereological axiomatic granular perspectives [5,6], classical granular computing, interpretations of modal logic [13], constructive logic,
concept analysis [14,6], evidence theory, and machine learning. These ideas ofknowledge are not uniformly well-developed across these types of rough sets.
Possible applications of these to education research, and specifically mathematical education research is a very domain-specific matter. Many aspects of theseare explored by the present author in her earlier papers [7,11,8,9].
Application of rough sets and other artificial intelligence methodologies to
education research is important for all the fields involved. This is because of
the intense complexity of knowledge representation in education research. The
subject is substantially population specific, and is strongly influenced by socioeconomic, psychological, and cultural factors. The very idea of core content
for a level of learning as understood from the perspective of a subject specialist
is not reasonably definable without additional constraints. However, the idea ofcore content in mathematics (for example) for regular courses in middle schoolor higher can be specified though not exactly as one would want to. Details of
proposed models are relegated to the full version of this report.
In the modern view [1], three components that determine a teacher’s knowledge base are knowledge of mathematics content, knowledge of student epistemology, and pedagogical knowledge. The first is about the breadth and depthof a teacher’s knowledge of the subject. The second concerns understanding of
the psychological principles of learning, concept formation, assimilation, andmaturation. Teacher’s understanding of teaching in the light of the other components is knowledge of pedagogy. Many instructive application of the concepts
that demonstrate how a teacher’s fixation with computational procedures forunderstanding can adversely affect conceptual learning outcomes of the class
may be found in the literature. Further associations between a teacher’s beliefs
about the nature of the subject (such as school mathematics is a fixed set of concepts and procedures that are to be delivered to and remembered by students),
and the fixations are deducible. These aspects matter for both the training of,⋆This research is supported by Woman Scientist Grant No. WOS-A/PM-22/2019 of the
Department of Science and Technology.

38

and designing intelligent software aids for teachers (and additionally for thedevelopment of curricula and textbooks).
A student’s understanding of a mathematics textbook in relation to the instructional context is distinct from a teacher’s understanding of the pedagogic
content (including psychological aspects) and context – the latter, in turn typically differs from a subject expert’s view on the matter (see [1] for a detailed
discussion). The categories of students, teachers and experts may be classifiable into a number of finer categories on the basis of subsets of features. The
classification of conceptual knowledge as evidenced by the work produced bylearners (using any form of expression or in any settings or context) takes many
forms. While concept inventories are useful to access over many types of contexts, they do not typically have the breadth to consider atypical reasoning, andrelated errors. Teachers understanding of the pedagogic content is in relation to allthese, and specifically to the meta-level language used for the context. The purpose ofthis research to propose a framework for this idealized understanding. Teachers and students, in a classroom, actually approximate a body of knowledge
formed in a distributed cognition perspective [12] in different ways.
The real numbers are taught in certain languages with much interspersedvagueness in middle school. Further, mathematical problem-solving methods,
and concepts have vagueness in related discourse. Making use of predicatessuch as is an approximation of, is a superset of, is a subset of, and is definitely apart of much of the formalization is possible. The harder problem of expressing
them through general rough approximations is additionally explored in this research after formalizing the school real numbers typically ignored by formal
mathematicians. Work in the area is primarily limited because of the need tosystematically confront the varieties of vagueness inherent in it [4,2]. However,there is much work on pragmatic approaches to the ontology of the real numbers in the context. They serve as sources for building viable models.
In the context of mathematics education research, the very idea of formal
language or model is open for some debate. In [9], it is argued that mereology
combined with a language of approximations can potentially be used to build
higher order formalizations of concepts that go beyond the restrictions envisaged in [4] or in earlier work [2].
Further, a common practice in teaching mathematics is to use everyday language when describing and explaining ideas. Teachers may use phrases such asplug in a value to evaluate a function, and cancel instead of dividing by the common factor. These and the overuse of pronouns can obscure the meaning of theprocedures and concepts being used. The use of imprecise, loose language, andunidentified errors by teachers can be counterproductive. These and related
studies motivate the need to build reasonably formal models to study teachers’
knowledge of content, and more so for the purpose of building intelligent aids
for teachers. ML approaches based on numeric simplifications of language features or keywords cannot succeed in the context, and none are attempted in theliterature.

39

The following are done in the full version of this research (within the domain of pedagogic knowledge):• A much improved applicable model of the vague version of real numbers
is proposed;• The model is intended for use with a companion model that involves multiple general rough approximation operators, mereological and rough predicates (possibly functional), and• a justification of aspects of the above is argued for through equational reasoning contexts.
Let a representation of the students’ knowledge of content be SC, and theobjective content be C. Now, an ideal teacher of the subject can be required
to know a lot more about C, the learning of SC by a student of type τ ∈ T(where T is a categorization of students by their sociocultural context) TC, andthe relation of SC to the teacher’s understanding of the content T, and relevant
concepts Co. Partial models MP of Co, and sufficient models MS of the idealteachers’ knowledge of content T may be constructed from the information.
Thus, the components of the ecosystem may be represented by the interrelationship diagram Fig 1 (the hierarchies are left to the reader to understand).

MP

MS

C

Co

TC

SC

T

Fig. 1: Components of the Ecosystem

The proposed system consists of three major blocks:• an improved better suited description of the real numbers as understood inschool mathematics,• formal systems for reasoning about mathematical assertions that accommodate ontology, mereology and vagueness, and psychosocial factors, and• a granular extension of the same based on earlier work of the present author.
Student-centric rough concept inventories [7] can additionally be handled moresmoothly through the companion models introduced as automatic evaluation40

of explanations is dependent on the latter. The coherent formalizability issue inthe education research literature is thus addressed to an extent. Higher-orderformalizations of equational reasoning (including [12]), described in detail in
the full version, are not unique, and making these more unique is an important problem. Issues relating to use of multiple approximation operators, ideasof overlap predicates, generalized overlap functions, weak rough implications,rationality, and substantial parthood [10], will be investigated further in future
work. Additionally, this research motivates higher order approaches in roughsets, that would have been previously glossed over through hybrid layers.

References
1. Ball, D., Thames, M., Phelps, G.: Content Knowledge for Teaching: What Makes it
Special? Journal of Teacher Education 59(5), 389–407 (2008)
2. Barwell, R., Others (eds.): Mathematics Education and Language Diversity –The 21st
ICMI Study. Springer (2021). https://doi.org/10.1007/978-3-319-14511-2
3. Harel, G., Lim, K.H.: Mathematics teachers’ knowledge base:preliminary results. In:
Proceeedings of 28th PME. vol. 3, pp. 25–32 (2004)
4. Jayasree, S., Subramaniam, K., Ramanujam, R.: Coherent Formalisability as Acceptability Criterion for Students’ Mathematical Discourse. Research in Mathematics Education (2022). https://doi.org/10.1080/14794802.2022.2041469
5. Mani, A.: Algebraic Methods for Granular Rough Sets. In: Mani, A., Düntsch, I.,
Cattaneo, G. (eds.) Algebraic Methods in General Rough Sets, pp. 157–336. Trends
in Mathematics, Birkhauser Basel (2018)
6. Mani, A.: Dialectical Rough Sets, Parthood and Figures of Opposition-I. Transactions
on Rough Sets XXI(LNCS 10810), 96–141 (2018)
7. Mani, A.: Towards Student Centric Rough Concept Inventories. In: Bello, R., et al.
(eds.) IJCRS’ 2020, LNAI, vol. 12179, pp. 251–266. Springer International (2020)
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11. Mani, A.: Mereological methods for education research, and school and college-level
mathematics. ICME, Accepted 15, 1–8 (2023)
12. Mani, A.: Rough algebraic semantics of concepts in a distributed cognition perspective. In: Hu, M., Others (eds.) Rough Sets IJCRS 2024, pp. 16–30. Springer Nature(2024)
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14. Yao, Y.: Rough-Set Concept Analysis: Interpreting Rs-Definable Concepts Based on
Ideas From Formal Concept Analysis. Information Sciences 347, 442–462 (2016)41

IJCRS 2024

Author Index

Author Index

Cabitza, Federico
Campagner, Andrea
Chouhan, Ankit
Ciucci, Davide
Cui, Caixia

1
1
6
1
33

Fisher, Andrew

10

Lingras, Pawan
Mani, A
Madrid, Nicolas
Mago, Vijay
Medina, Jesús

18
38
14
10, 1814

Neveditsin, Nikita
Nguyen, Hung Son

18
26

Ramı́rez Poussa, Eloisa
Rao, Hong

14
35

Slezak, Dominik

22

Vo, Bich Khue

26

Wang, Baoli
Wang, Hao
Wang, Pengling
Wang, Ruihua

33
33
33
35

Xu, Jianfeng

35

Yao, Jingtao

61

IJCRS 2024

Keyword Index

Keyword Index

Approximation operators
Artificial Intelligence
Aspect-Based Sentiment Analysis
Attention Mechanism
Attribute ranking

14
6
18
33
26

Concept Drift35

data analytics
Data Labeling
Deep Learning
Detection
Domain Adaption

10
22
33
35
33

Federated Learning
Fuzzy preference relation

626

Galois connection

14

Interactive ML

22

LITL
LLM

22
18

Machine Learning

1

Metrics
Multi-adjoint property-oriented concept lattice

18
14

NLP

18

Optimization models

26

Privacy-Preserving
public health610risk monitoring
Rough fuzzy hybridization
Rough sets

10
26
14

Sentimend Analysis
social media
Streaming Data

18
10
35
1

IJCRS 2024

2

Keyword Index

Structural Health Monitoring

33

Three-way decision
Transfer Learning

1
33

Uncertainty

1