pwc-archive/papers-with-abstracts huggingface dataset:
Learning with local drift detection
Authors: João Gama, Gladys Castillo
Date: 2006-08-01 00:00:00
Proceeding: Advanced Data Mining and Applications, Second International Conference, ADMA 2006 8
Tasks: Drift Detection

Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generates the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to monitor the online error-rate of a learning algorithm looking for significant deviations. The method can be used as a wrapper over any learning algorithm. In most problems, a change affects only some regions of the instance space, not the instance space as a whole. In decision models that fit different functions to regions of the instance space, like Decision Trees and Rule Learners, the method can be used to monitor the error in regions of the instance space, with advantages of fast model adaptation. In this work we present experiments using the method as a wrapper over a decision tree and a linear model, and in each internal-node of a decision tree. The experimental results obtained in controlled experiments using artificial data and a real-world problem show a good performance detecting drift and in adapting the decision model to the new concept.
Early Drift Detection Method
Authors: Manuel Baena-Garcia, Jose del Campo- Avila, Raul Fidalgo, Albert Bifet, Ricard Gavalda, and Rafael Morales-Bueno
Date: 2006-01-01 00:00:00
Proceeding: Fourth International Workshop on Knowledge Discovery from Data Streams 2006 1
Tasks: Drift Detection

An emerging problem in Data Streams is the detection of
concept drift. This problem is aggravated when the drift is gradual over
time. In this work we de¯ne a method for detecting concept drift, even in
the case of slow gradual change. It is based on the estimated distribution
of the distances between classi¯cation errors. The proposed method can
be used with any learning algorithm in two ways: using it as a wrapper
of a batch learning algorithm or implementing it inside an incremental
and online algorithm. The experimentation results compare our method
(EDDM) with a similar one (DDM). Latter uses the error-rate instead
of distance-error-rate.
Learning and Detecting Concept Drift
Authors: Kyosuke Nishida
Date: 2008-02-01 00:00:00
Proceeding: Graduate School of Information Science and Technology, Hokkaido University 2008 2

The volume of data that humans create has increased explosively as information
science and technology have evolved. Therefore, the demand for learning machines that
can extract input-output mappings and knowledge rules from massive data sets has
become more urgent, and machine learning is now a core technology in the advanced
information society. It has been applied to fields such as pattern recognition, search
engines, medical support, robot engineering, image processing, and data mining and
has achieved significant accomplishments in each field. Recently, several methods that
could not be implemented with older computers have been developed with state-of-the-art computers that have enormous memory capacity and high performance CPUs.
Machine learning is expected to continue to develop in the future.
...
We propose a simpler method of more accurately detecting concept drift.
The STEPD method monitors the predictive accuracy of a single online classifier and
detects significant decreases in the predictive accuracy, which are caused by concept
drift, by using a statistical test of equal proportions to detect concept drift. When
concept drift is detected, the online classifier is reinitialized to prepare for the learning
of the next concept. STEPD is able to detect concept drift more quickly and accurately
than ACE and other methods, but false alarms degrade the predictive accuracy of
STEPD significantly because STEPD uses only a single online classifier.
Hellinger distance based drift detection for nonstationary environments
Authors: Gregory Ditzler, Robi Polikar
Date: 2011-04-01 00:00:00
Proceeding: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments 2011 4
Tasks: Drift Detection

Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed. There are many real-world problems where the distribution of the data changes as a function of time. Changes in nonstationary data distributions can significantly reduce the generalization ability of the learning algorithm on new or field data, if the algorithm is not equipped to track such changes. When the stationary data distribution assumption does not hold, the learner must take appropriate actions to ensure that the new/relevant information is learned. On the other hand, data distributions do not necessarily change continuously, necessitating the ability to monitor the distribution and detect when a significant change in distribution has occurred. In this work, we propose and analyze a feature based drift detection method using the Hellinger distance to detect gradual or abrupt changes in the distribution.
An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams
Authors: Tamraparni Dasu, Shankar Krishnan, Suresh Venkatasubramanian, Ke Yi
Date: 2006-01-01 00:00:00
Proceeding: INFORMS 2006 1
Tasks: Change Detection

An important problem in processing large data streams is detecting changes in the underlying distribution that generates the data. The challenge in designing change detection schemes
is making them general, scalable, and statistically sound. In this paper, we take a general,
information-theoretic approach to the change detection problem, which works for multidimensional as well as categorical data. We use relative entropy, also called the Kullback-Leibler
distance, to measure the difference between two given distributions. The KL-distance is known
to be related to the optimal error in determining whether the two distributions are the same
and draws on fundamental results in hypothesis testing. The KL-distance also generalizes traditional distance measures in statistics, and has invariance properties that make it ideally suited
for comparing distributions.
Our scheme is general; it is nonparametric and requires no assumptions on the underlying
distributions. It employs a statistical inference procedure based on the theory of bootstrapping,
which allows us to determine whether our measurements are statistically significant. The scheme
is also quite flexible from a practical perspective; it can be implemented using any spatial partitioning scheme that scales well with dimensionality. In addition to providing change detections,
our method generalizes Kulldorff’s spatial scan statistic, allowing us to quantitatively identify
specific regions in space where large changes have occurred.
We provide a detailed experimental study that demonstrates the generality and efficiency of
our approach with different kinds of multidimensional datasets, both synthetic and real.
A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams
Authors: Abdulhakim A. Qahtan, Basma Alharbi, Suojin Wang, Xiangliang Zhang
Date: 2015-08-01 00:00:00
Proceeding: ACM SIGKDD international conference on Knowledge discovery and data mining 2015 8
Tasks: Change Detection, Density Estimation

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations.

The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.
Drift detection using uncertainty distribution divergence
Authors: Patrick Lindstrom, Brian Mac Namee, Sarah Jane Delany
Date: 2011-12-01 00:00:00
Proceeding: Evolving Systems 2011 12
Tasks: Drift Detection

Data generated from naturally occurring processes tends to be non-stationary. For example, seasonal and gradual changes in climate data and sudden changes in financial data. In machine learning the degradation in classifier performance due to such changes in the data is known as concept drift and there are many approaches to detecting and handling it. Most approaches to detecting concept drift, however, make the assumption that true classes for test examples will be available at no cost shortly after classification and base the detection of concept drift on measures relying on these labels. The high labelling cost in many domains provides a strong motivation to reduce the number of labelled instances required to detect and handle concept drift. Triggered detection approaches that do not require labelled instances to detect concept drift show great promise for achieving this. In this paper we present Confidence Distribution Batch Detection, an approach that provides a signal correlated to changes in concept without using labelled data. This signal combined with a trigger and a rebuild policy can maintain classifier accuracy which, in most cases, matches the accuracy achieved using classification error based detection techniques but using only a limited amount of labelled data.
A Fast Greedy Algorithm for Outlier Mining
Authors: Zengyou He, Shengchun Deng, Xiaofei Xu, Joshua Zhexue Huang
Date: 2006-04-01 00:00:00
Proceeding: Advances in Knowledge Discovery and Data Mining 2006 4
Tasks: Outlier Detection

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.
Bangla Parts-of-Speech Tagging using Bangla Stemmer and Rule based Analyzer
Authors: Md. Nesarul Hoque, Md. Hanif Seddiqui
Date: 2016-06-09 00:00:00
Proceeding: 18th International Conference on Computer and Information Technology (ICCIT) 2016 6
Tasks: Emotion Recognition, Information Retrieval, Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging, Retrieval, TAG

Parts-of-Speech (POS) tagging plays vital roles in the field of Natural Language Processing (NLP), such as - machine translation, spell checker, information retrieval, speech processing, emotion analysis and so on. Bangla is a very inflectional language that induces many variants from a single word. Although there is a few POS Tagger in Bangla language,very small of them address the essence of suffices to identify tag of the words. In this regard, we propose an automated POS Tagging system for Bangla language based on word-suffixes. In our system, we use our own stemming technique to retrieve a possible minimum root words and apply rules according to different forms of suffixes. Moreover, we incorporate a Bangla vocabulary that contains more than 45,000 words with their default tag and a patterned based verb-data-set. These facilitate to improve tagging efficiency of Bangla POS Tagger. We experiment our proposed system on a Bangla text corpus. The result shows that our proposed Bangla POS Tagger has outperformed the known related tagging systems.
Selective transfer learning with adversarial training for stock movement prediction
Authors: Yang Li, Hong-Ning Dai, Zibin Zheng
Date: 2021-09-30 00:00:00
Proceeding: Taylor & Francis Online 2021 9
Tasks: Prediction, Stock Prediction, Transfer Learning

Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction result. This paper contributes a new stock movement prediction model, Selective Transfer Learning with Adversarial Training (STLAT). Our STLAT method advances existing solutions in two major aspects: (i) tailoring the pre-trained and fine-tuned method for stock movement prediction and (ii) introducing the data selector module to select the more relevant training samples. More specifically, we pre-train the shared base model using three different tasks. The predictor task is constructed to measure the performance of the shared base model with source domain data and target domain data. The adversarial training task is constructed to improve the generalisation of the shared base model. The data selector task is introduced to select the most relevant and high-quality training samples from stocks in source domain. All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset. Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively.
Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo
Authors: Jaehee Kim · Sooyoung Cheon
Date: 2010-06-01 00:00:00
Proceeding: journal 2010 6

Bayesian multiple change-point models are built with data from normal, exponential, binomial and Poisson distributions with a truncated Poisson prior for the number of change-points and conjugate prior for the distributional parameters. We applied Annealing Stochastic Approximation Monte Carlo (ASAMC) for posterior probability calculations for the possible set of change-points. The proposed methods are studied in simulation and applied to temperature and the number of respiratory deaths in Seoul, South Korea.
Human activity recognition using improved dynamic image
Authors: Mohammadreza Riahi, Mohammad Eslami, Seyed Hamid Safavi, Farah Torkamani Azar
Date: 2020-11-15 00:00:00
Proceeding: IET Image Processing 2020 11
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition

In action recognition, the dynamic image (DI) approach is recently proposed to code a video signal to a still image. Since DI descriptor is strongly dependent on first frames, it cannot extract dynamics that do not occur in the first frames or even long dynamics. On the other hand, most of the video frames are not informative for the task of action recognition. Therefore, the authors' intuition is that the process of representing a video using all frames is inefficient. Thus, in this study, they proposed to remove the existing redundancy inside the frames and extract some processed informative images based on the information theory which are called key frames. The proposed method is capable enough to extract sufficient frames regardless of the duration and the position of frames in the entire video. Motivated by this method and DI, they proposed a novel key frames dynamic image (KFDI) approach. Experimental results on popular UCF11, Olympic Sports, and J-HMDB datasets show the superiority of the proposed KFDI approach compared to the DI in capturing long dynamics of videos for action recognition. Their experiments show KFDI improves the accuracy 2–6% compared to DI.
Structured and Natural Responses Co-generation for Conversational Search
Authors: Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua
Date: 2022-07-07 00:00:00
Proceeding: ACM SIGIR Conference on Research and Development in Information Retrieval 2022 7
Tasks: Conversational Search

Generating fluent and informative natural responses while maintaining representative internal states for search optimization is critical for conversational search systems. Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses directly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural responses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that generates the two concurrently. The key lies in a shared latent space shaped by two informed priors. Specifically, we design structured dialog acts and natural response auto-encoding as two auxiliary tasks in an interconnected network architecture. It allows for the concurrent generation and bidirectional semantic associations. The shared latent space also enables asynchronous reinforcement learning for further joint optimization. Experiments show that our model achieves significant performance improvements.
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
Authors: Ricardo A. Gonzales, Jérôme Lamy, Felicia Seemann, Einar Heiberg, John A. Onofrey, Dana C. Peters
Date: 2021-09-21 00:00:00
Proceeding: International Conference on Medical Image Computing and Computer-Assisted Intervention 2021 9

Tracking the tricuspid valve (TV) in magnetic resonance imaging (MRI) long-axis cine images has the potential to aid in the evaluation of right ventricular dysfunction, which is common in congenital heart disease and pulmonary hypertension. However, this annotation task remains difficult and time-demanding as the TV moves rapidly and is barely distinguishable from the myocardium. This study presents TVnet, a novel dual-stage deep learning pipeline based on ResNet-50 and automated image linear transformation, able to automatically derive tricuspid annular plane systolic excursion. Stage 1 uses a trained network for a coarse detection of the TV points, which are used by stage 2 to reorient the cine into a standardized size, cropping, resolution, and heart orientation and to accurately locate the TV points with another trained network. The model was trained and evaluated on 4170 images from 140 patients with diverse cardiovascular pathologies. A baseline model without standardization achieved a Euclidean distance error of 4.0 ± 3.1 mm and a clinical-metric agreement of ICC = 0.87, whereas a standardized model improved the agreement to 2.4 ± 1.7 mm and an ICC = 0.94, on par with an evaluated inter-observer variability of 2.9 ± 2.9 mm and an ICC = 0.92, respectively. This novel dual-stage deep learning pipeline substantially improved the annotation accuracy compared to a baseline model, paving the way towards reliable right ventricular dysfunction assessment with MRI.
Physics-Aware Neural Networks for Boundary Layer Linear Problems
Authors: Antonio Tadeu Azevedo Gomes, Larissa Miguez da Silva, Frederic Valentin
Date: 2022-07-15 00:00:00

Physics-Informed Neural Networks (PINNs) are machine learning tools that approximate the solution of general partial differential equations (PDEs) by adding them in some form as terms of the loss/cost function of a Neural Network. Most pieces of work in the area of PINNs tackle non-linear PDEs. Nevertheless, many interesting problems involving linear PDEs may benefit from PINNs; these include parametric studies, multi-query problems, and parabolic (transient) PDEs. The purpose of this paper is to explore PINNs for linear PDEs whose solutions may present one or more boundary layers. More specifically, we analyze the steady-state reaction-advection-diffusion equation in regimes in which the diffusive coefficient is small in comparison with the reactive or advective coefficients. We show that adding information about these coefficients as predictor variables in a PINN results in better prediction models than in a PINN that only uses spatial information as predictor variables. This finding may be instrumental in multiscale problems where the coefficients of the PDEs present high variability in small spatiotemporal regions of the domain, and therefore PINNs may be employed together with domain decomposition techniques to efficiently approximate the PDEs locally at each partition of the spatiotemporal domain, without resorting to different learned PINN models at each of these partitions.
Tinjauan atas Efektivitas Penggunaan Key Opinion Leader (KOL) dalam Penjualan Surat Utang Negara Ritel seri SBR011
Authors: Dea Avega Editya
Date: 2022-08-13 00:00:00
Tasks: Marketing, Sentiment Analysis

Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed effectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social media Instagram and TikTok social platform used by the KOL to post their marketing contents. This paper found that the endorsement is quite effective to promote the SBR011 and yields mostly positive feedback on the marketing campaign.