pwc-archive/papers-with-abstracts huggingface dataset:
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package
Authors: F.J. Martinez-de-Pison, J. Ferreiro, E. Fraile, A. Pernia-Espinoza
Date: 2021-09-10 00:00:00
Proceeding: Neurocomputing 2021 9
Tasks: Feature Engineering, feature selection, Hyperparameter Optimization

Nowadays, interest is growing in automating KDD processes. Thanks to the increasing power and decreasing costs of computation devices, the search for the best features and model parameters can be conducted with different meta-heuristics. Thus, researchers can focus on other important tasks like data wrangling or feature engineering. This article details a comparative study of a GAparsimony R Package with six model complexity metrics. The objective was to identify an adequate model complexity measure for searching for accurate parsimonious solutions by combining feature selection, hyperparameter optimization, and parsimonious evaluation. This study also includes a regression code example to address some recommended precautions and considerations to find robust parsimonious models. This code can be easily adapted to other problems, databases, or algorithms.
An advanced methodology to enhance energy efficiency in a hospital cooling-water system
Authors: E. Dulce-Chamorro, F.J. Martinez-de-Pison
Date: 2021-11-01 00:00:00
Proceeding: Journal of Building Engineering 2021 11
Tasks: Scheduling

Healthcare facilities consume massive amounts of energy. This study outlines a methodology to enhance energy efficiency and solve common problems in hospital cooling-water systems, since hospitals are the most energy-intensive type of building. Building Management Systems (BMS) are a widely used technique to control and monitor all the different energy facilities contained in hospitals. Proper setup and upgrades can resolve inefficiencies and existing problems. The methodology described herein addresses the general cooling system adjustments in three main areas: control system (CS), data acquisition system (DAS), and physical system (PS). An innovative feature incorporated in this methodology is the cooling demand model integrated into the CS, which is capable of forecasting and transmitting a schedule for maximum thermal energy requirements to the BMS a day in advance, thereby anticipating decisions and scheduling energy generation and maintenance operations. During the process of developing the cooling demand model, various machine learning models were trained. This process consisted of searching for low-complexity models using a methodology called GAparsimony. This methodology uses genetic algorithms to search for highly precise, robust models that use a low input. The final model consisted of a weighted combination of Artificial Neural Network (ANN) and Support Vector Regression (SVR) models. The energy savings obtained thanks to this methodology are estimated to be between 7% and 10% per year. The energy plant improved its performance and chiller starts were reduced by 82.5%. It should also be noted that this study was affected by the recommendations for increased ventilation due to the COVID-19 pandemic, which entailed at 22.4% increase in energy consumption in 2020. The methodology was developed and tested successfully in a real hospital BMS between 2017 and 2019; the model was finally integrated in 2020.
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection
Authors: F.J. Martinez-de-Pison, R. Gonzalez-Sendino, A Aldama, J. Ferreiro-Cabello, E. Fraile-Garcia
Date: 2019-08-18 00:00:00
Proceeding: Neurocomputing 2019 8
Tasks: Bayesian Optimization, feature selection, Hyperparameter Optimization, Model Selection

This article presents a hybrid methodology that combines Bayesian optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimony models. The proposal is designed to reduce the sizeable computational effort associated with the use of GA-PARSIMONY alone. The method begins with BO to obtain favorable initial model parameters. Then, with these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimony models by using feature reduction, data transformation and parsimonious model selection. Experiments with extreme gradient boosting machines (XGBoost) and ten UCI databases demonstrated that the hybrid methodology obtains models analogous to those of GA-PARSIMONY while achieving significant reductions in elapsed time in eight out of ten datasets.
Evaluation of a novel GA-based methodology for model structure selection: The GA-PARSIMONY
Authors: R. Urraca, E. Sodupe-Ortega, J. Antonanzas, F. Antonanzas-Torres, F.J. Martinez-de-Pison
Date: 2018-01-03 00:00:00
Proceeding: Neurocomputing 2018 1
Tasks: feature selection, regression

Most proposed metaheuristics for feature selection and model parameter optimization are based on a two-termed 'loss+penalty' function. Their main drawback is the need of a manual set of the parameter that balances between the loss and the penalty term. In this paper, a novel methodology referred as the GA-PARSIMONY and specifically designed to overcome this issue is evaluated in detail in thirteen public databases with five regression techniques. It is a GA-based meta-heuristic that splits the classic two-termed minimization functions by making two consecutive ranks of individuals. The first rank is based solely on the generalization error, while the second (named ReRank) is based on the complexity of the models, giving a special weight to the complexity entailed by large number of inputs. For each database, models with lowest testing RMSE and without statistical difference among them were referred as winner models. Within this group, the number of features selected was below 50%, which proves an optimal balance between error minimization and parsimony. Particularly, the most complex algorithms (MLP and SVR) were mostly selected in the group of winner models, while using around40–45% of the available attributes. The most basic IBk, ridge regression (LIN) and M5P were only classified as winner models in the simpler databases, but using less number of features in those cases (up to a 20–25% of the initial inputs).
Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components
Authors: A. Pernía-Espinoza, J. Fernandez-Ceniceros, J.  Antonanzas, R. Urraca, F.J. Martinez-de-Pison
Date: 2018-09-01 00:00:00
Proceeding: Applied Soft Computing Journal 2018 9
Tasks: Diversity, feature selection, Hyperparameter Optimization

This study presents a new soft computing method to create an accurate and reliable model capable of determining three key points of the comprehensive force–displacement curve of bolted components in steel structures. To this end, a database with the results of a set of finite element (FE) simulations, which represent real responses of bolted components, is utilized to create a stacking ensemble model that combines the predictions of different parsimonious base models. The innovative proposal of this study is using GA-PARSIMONY, a previously published GA-method which searches parsimonious models by optimizing feature selection and hyperparameter optimization processes. Therefore, parsimonious solutions created with a variety of machine learning methods are combined by means of a nested cross-validation scheme in a unique meta-learner in order to increase diversity and minimize the generalization error rate. The results reveal that efficiently combining parsimonious models provides more accurate and reliable predictions as compared to other methods. Thus, the informational model is able to replace costly FE simulations without significantly comprising accuracy and could be implemented in structural analysis software. © 2018 Elsevier B.V.
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning
Authors: A. Pernía-Espinoza, J. Fernandez-Ceniceros, J. Antonanzas, R. Urraca, F.J. Martinez-de-Pison
Date: 2017-09-06 00:00:00
Proceeding: Logic Journal of the IGPL 2017 9
Tasks: Demand Forecasting, feature selection, Model Selection

This article presents a hybrid methodology in which a KDD scheme is optimized to build accurate parsimonious models. The methodology tries to find the best model by using genetic algorithms to optimize a KDD scheme formed with the following stages: feature selection, transformation of the skewed input and the output data, parameter tuning and parsimonious model selection. The results obtained demonstrated the optimization of these steps that significantly improved the model generalization capabilities in some UCI databases. Finally, this methodology was applied to create room demand parsimonious models using booking databases from a hotel located in a region of Northern Spain. The results proved that the proposed method created models with higher generalization capacity and lower complexity compared to those obtained with classical KDD process.
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases
Authors: F.J. Martinez-de-Pison, E. Fraile-Garcia, J. Ferreiro-Cabello, R. Gonzalez, A. Pernia
Date: 2016-10-01 00:00:00
Proceeding: Advances in Intelligent Systems and Computing 2016 10
Tasks: feature selection, Model Selection

EXtreme Gradient Boosting (XGBoost) has become one of the most successful techniques in machine learning competitions. It is computationally efficient and scalable, it supports a wide variety of objective functions and it includes different mechanisms to avoid overfitting and improve accuracy. Having so many tuning parameters, soft computing (SC) is an alternative to search precise and robust models against classical hyper-tuning methods. In this context, we present a preliminary study in which a SC methodology, named GA-PARSIMONY, is used to find accurate and parsimonious XGBoost solutions. The methodology was designed to optimize the search of parsimonious models by feature selection, parameter tuning and model selection. In this work, different experiments are conducted with four complexity metrics in six high dimensional datasets. Although XGBoost performs well with high-dimensional databases, preliminary results indicated that GAPARSIMONY with feature selection slightly improved the testing error. Therefore, the choice of solutions with fewer inputs, between those with similar cross-validation errors, can help to obtain more robust solutions with better generalization capabilities.
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace
Authors: A. Sanz-Garcia, J. Fernandez-Ceniceros, F. Antonanzas-Torres, A. Pernia-Espinoza, F.J. Martinez-de-Pison
Date: 2015-10-01 00:00:00
Proceeding: Applied Soft Computing Journal 2015 10
Tasks: feature selection, Model Selection

This article proposes a new genetic algorithm (GA) methodology to obtain parsimonious support vector regression (SVR) models capable of predicting highly precise setpoints in a continuous annealing furnace (GA-PARSIMONY). The proposal combines feature selection, model tuning, and parsimonious model selection in order to achieve robust SVR models. To this end, a novel GA selection procedure is introduced based on separate cost and complexity evaluations. The best individuals are initially sorted by an error fitness function, and afterwards, models with similar costs are rearranged according to model complexity measurement so as to foster models of lesser complexity. Therefore, the user-supplied penalty parameter, utilized to balance cost and complexity in other fitness functions, is rendered unnecessary. GA-PARSIMONY performed similarly to classical GA on twenty benchmark datasets from public repositories, but used a lower number of features in a striking 65% of models. Moreover, the performance of our proposal also proved useful in a real industrial process for predicting three temperature setpoints for a continuous annealing furnace. The results demonstrated that GA-PARSIMONY was able to generate more robust SVR models with less input features, as compared to classical GA.
A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel
Authors: J. Fernandez-Ceniceros, A. Sanz-Garcia, F. Antoñanzas-Torres, F.J. Martinez-de-Pison
Date: 2015-01-01 00:00:00
Proceeding: Engineering Structures 2015 1
Tasks: feature selection

The accuracy of the component-based method relies heavily on the characteristic response of their constitutive elements. To properly assess the deformation capacity of the whole connection, modelling the complete force-displacement curves of the components, from the initial stiffness to fracture, is necessary. This paper presents a numerical-informational method for calculating the ductile response of the T-stub component. In order to reduce the intensive computation of the finite element (FE) method, the results of numerical simulations are used to train a set of metamodels based on soft-computing (SC) techniques. These metamodels are capable of predicting, with a high degree of accuracy, the key parameters that define the force-displacement curve of the T-stub. In addition, a feature selection (FS) scheme based on genetic algorithms (GAs) is included in the training process to select the most influential input variables. This scheme leads to overall and parsimonious metamodels that improve the method's generalisation capacity.The mean absolute error (MAE) in the prediction of each key parameter reports values below 5% for both validation and test results. This demonstrates the strong performance of the SC-based metamodels when comparing them with the FE simulations. Finally, this hybrid method constitutes a suitable tool to be implemented in non-linear steel connections software.
PALS: Personalized Active Learning for Subjective Tasks in NLP
Authors: Kamil Kanclerz, Konrad Karanowski, Julita Bielaniewicz, Marcin Gruza, Piotr Miłkowski, Jan Kocon, Przemyslaw Kazienko
Date: 2023-12-08 00:00:00
Proceeding: Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023 12
Tasks: Active Learning

For subjective NLP problems, such as classification of hate speech, aggression, or emotions, personalized solutions can be exploited. Then, the learned models infer about the perception of the content independently for each reader. To acquire training data, texts are commonly randomly assigned to users for annotation, which is expensive and highly inefficient. Therefore, for the first time, we suggest applying an active learning paradigm in a personalized context to better learn individual preferences. It aims to alleviate the labeling effort by selecting more relevant training samples. In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect. Our five new measures allow us to determine the relevance of a text in the context of learning users personal preferences. We validated them on three datasets: Wiki discussion texts individually labeled with aggression and toxicity, and on Unhealthy Conversations dataset. Our PALS techniques outperform random selection even by more than 30%. They can also be used to reduce the number of necessary annotations while maintaining a given quality level. Personalized annotation assignments based on our controversy measure decrease the amount of data needed to just 25%-40% of the initial size.
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Authors: Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, Li Jiang
Date: 2023-06-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Data Augmentation, Offline RL, Reinforcement Learning (RL)

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.Code is available at: https://github.com/pcheng2/TSRL
Clustering Urdu News Using Headlines
Authors: Samia Khaliq, Waheed Iqbal, Faisal Bukhari, Kamran Malik
Date: 2015-09-27 00:00:00
Proceeding: 23 2015 9
Tasks: Clustering, Information Retrieval, Text Clustering

This paper that proposes and evaluates a new algorithm to automatically cluster Urdu news from different news agencies. The task is challenging because there are no language processing libraries for the Urdu language. The authors' experimental dataset consists of news from famous Pakistani media houses, including Jang, BBC Urdu, Express, UrduPoint, and Voice of America Urdu (VOA). The proposed algorithm only uses headlines to cluster the news. The authors argue that news headlines provide a concise summary of the news, which motivates them to use it instead of using the entire news story. Their experimental evaluation shows micro and macro averages for precision of 0.45 and 0.48 respectively for identifying similar news using headlines.
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
Authors: Pengfei Wei, Lingdong Kong, Xinghua Qu, Yi Ren, Zhiqiang Xu, Jing Jiang, Xiang Yin
Date: 2022-08-15 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Action Recognition, Disentanglement, Domain Adaptation

Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the spatial and temporal domain divergence separately through disentanglement. Specifically, we consider the generation of cross-domain videos from two sets of latent factors, one encoding the static information and another encoding the dynamic information. A Transfer Sequential VAE (TranSVAE) framework is then developed to model such generation. To better serve for adaptation, we propose several objectives to constrain the latent factors. With these constraints, the spatial divergence can be readily removed by disentangling the static domain-specific information out, and the temporal divergence is further reduced from both frame- and video-levels through adversarial learning. Extensive experiments on the UCF-HMDB, Jester, and Epic-Kitchens datasets verify the effectiveness and superiority of TranSVAE compared with several state-of-the-art approaches. Code is publicly available.
Detecting Emotions Through Machine Learning for Automatic UX Evaluation
Authors: Giuseppe Desolda, Andrea Esposito, Rosa Lanzilotti, and Maria F. Costabile
Date: 2021-08-26 00:00:00
Proceeding: 18th IFIP TC 13 Conference on Human-Computer Interaction (INTERACT) 2021 8

Although User eXperience (UX) is widely acknowledged as an important aspect of software products, its evaluation is often neglected during the development of most software products, primarily because developers think that it is resource-demanding and complain about the fact that is scarcely automated. Various attempts have been made to develop tools that support and automate the execution of tests with users. This paper is about an ongoing research work that exploits Machine Learning (ML) for automatic UX evaluation, specifically for understanding users’ emotions by analyzing the log data of the users’ interactions with websites. The approach described aims at overcoming some limitations of existing proposals based on ML.
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET
Authors: Giovanna Castellano, Andrea Esposito, Marco Mirizio, Graziano Montanaro, Gennaro Vessio
Date: 2022-01-24 00:00:00
Proceeding: IEEE Symposium Series on Computational Intelligence (SSCI) 2022 1
Tasks: Medical Image Classification

Dementia is one of the most common diseases in the elderly and a leading cause of mortality and disability. In recent years, a research effort has been made to develop computer aided diagnosis tools based on machine (deep) learning models fed with neuroimaging data. However, while much work has been done on MRI imaging, very little attention has been paid on amyloid PETs, which have been recently recognized to be a promising and powerful biomarker of neurodegeneration. In this paper, we contribute to this less explored research area by proposing a 3D Convolutional Neural Network aimed at detecting dementia based on amyloid PET scans. An experiment performed on the recently released OASIS-3 dataset, which provides the community with a new benchmark to advance this line of research further, yielded very promising results and provided new evidence on the effectiveness of amyloid PET.
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction
Authors: Thaís Pedruzzi do Nascimento, Evandro Ottoni Teatini Salles
Date: 2020-10-23 00:00:00
Proceeding: IEEE Signal Processing Letters 2020 10
Tasks: Multi-Frame Super-Resolution, SSIM, Super-Resolution

This letter proposes a multi-frame super-resolution framework by combining Demons registration with Bayesian-based regularized reconstruction. For both of our proposals, D-BTVIR and D-IRWIR, the visual analysis shows improvements in regions where the compared methods produced results with either motion-artifacts or over-smoothed aspect due to misregistration. Quantitative results on simulated deformations show an improvement of 3.5%, on average, in PSNR and 8.0%, in SSIM. Finally, results from the Nemenyi test show that D-IRWIR is statistically superior to the other methods we tested, both considering SSIM and PSNR, and D-BTVIR is statistically equivalent while being 7 times faster than the state-of-the-art Bayesian method.