UNIVERSIDAD PABLO DE OLAVIDE
Publications
association rules astrostatistics big data bioinformatics clustering data streaming deep learning education energy feature selection forecasting IoT natural disasters pattern recognition time series transfer learning XAI
2022 |
A. Gómez-Losada and G. Asencio-Cortés and N. Duch-Brown Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm (Journal Article) Information, 13 (44), pp. 1–16, 2022. (Abstract | Links | BibTeX | Tags: feature selection, time series) @article{losada2022, title = {Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm}, author = {A. Gómez-Losada and G. Asencio-Cortés and N. Duch-Brown}, url = {https://www.mdpi.com/2078-2489/13/2/44}, doi = {10.3390/info13020044}, year = {2022}, date = {2022-01-01}, journal = {Information}, volume = {13}, number = {44}, pages = {1--16}, abstract = {Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.}, keywords = {feature selection, time series}, pubstate = {published}, tppubtype = {article} } Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box. |
J. Roiz-Pagador and A. Chacon-Maldonado and R. Ruiz and G. Asencio-Cortes Earthquake Prediction in California using Feature Selection techniques (Conference) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021), Advances in Intelligent Systems and Computing 2022. (Links | BibTeX | Tags: feature selection, natural disasters, time series) @conference{roiz2022, title = {Earthquake Prediction in California using Feature Selection techniques}, author = {J. Roiz-Pagador and A. Chacon-Maldonado and R. Ruiz and G. Asencio-Cortes}, url = {https://link.springer.com/chapter/10.1007/978-3-030-87869-6_69}, year = {2022}, date = {2022-01-01}, booktitle = {16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)}, series = {Advances in Intelligent Systems and Computing}, keywords = {feature selection, natural disasters, time series}, pubstate = {published}, tppubtype = {conference} } |
S. Gómez-Guerrero and I. Ortiz and G. and Sosa-Cabrera and M. García-Torres and C.E. Schaerer Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty (Journal Article) Entropy, 24 (1), pp. 64, 2022. (Abstract | Links | BibTeX | Tags: feature selection) @article{gomez2022measuring, title = {Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty}, author = {S. Gómez-Guerrero and I. Ortiz and G. and Sosa-Cabrera and M. García-Torres and C.E. Schaerer}, url = {https://www.mdpi.com/1099-4300/24/1/64}, doi = {10.3390/e24010064}, year = {2022}, date = {2022-01-01}, journal = {Entropy}, volume = {24}, number = {1}, pages = {64}, publisher = {Multidisciplinary Digital Publishing Institute}, abstract = {Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models. |
2021 |
R. Mortazavi and S. Mortazavi and A. Troncoso Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer (Journal Article) Engineering with Computers, 2021. (Abstract | Links | BibTeX | Tags: feature selection) @article{Mortazavi21, title = {Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer}, author = {R. Mortazavi and S. Mortazavi and A. Troncoso}, url = {https://link.springer.com/article/10.1007/s00366-020-01226-1}, doi = {10.1007/s00366-020-01226-1}, year = {2021}, date = {2021-01-01}, journal = {Engineering with Computers}, abstract = {This paper introduces different types of regression trees for viscosity property forecasting in polymer solutions. Although regression trees have been extensively used in other fields, they do not have been explored to predict the viscosity. One key issue in the context of materials science is to determine a priori which characteristics must be included to describe the prediction model due to a large number of molecular descriptors is obtained. To deal with this, we propose a wrapper method to select the features based on regression trees. Thus, we use regression trees to evaluate different subsets of attributes and build a model from the subset of features that achieved the minimum error. In particular, the performance of eight regression tree algorithms, including both linear and non-linear models, is evaluated and compared to other forecasting approaches using a dataset composed of 64 polymers and 2962 molecular descriptors. The results show that regression trees with nearest neighbors based local models in leaves predict with high accuracy. Moreover, results have been compared to other forecasting approaches such as multivariate linear regression, neural networks and support vector machines showing remarkable improvements in terms of accuracy.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } This paper introduces different types of regression trees for viscosity property forecasting in polymer solutions. Although regression trees have been extensively used in other fields, they do not have been explored to predict the viscosity. One key issue in the context of materials science is to determine a priori which characteristics must be included to describe the prediction model due to a large number of molecular descriptors is obtained. To deal with this, we propose a wrapper method to select the features based on regression trees. Thus, we use regression trees to evaluate different subsets of attributes and build a model from the subset of features that achieved the minimum error. In particular, the performance of eight regression tree algorithms, including both linear and non-linear models, is evaluated and compared to other forecasting approaches using a dataset composed of 64 polymers and 2962 molecular descriptors. The results show that regression trees with nearest neighbors based local models in leaves predict with high accuracy. Moreover, results have been compared to other forecasting approaches such as multivariate linear regression, neural networks and support vector machines showing remarkable improvements in terms of accuracy. |
S.A. Grillo and J.C. Román and J.D. Mello-Román and J.L. Vázquez Noguera and M. García-Torres and F. Divina and P.E. Sotomayor Adjacent Inputs With Different Labels and Hardness in Supervised Learning (Journal Article) IEEE Access, pp. 162487–162498, 2021. (Links | BibTeX | Tags: feature selection, pattern recognition) @article{grillo2021adjacent, title = {Adjacent Inputs With Different Labels and Hardness in Supervised Learning}, author = {S.A. Grillo and J.C. Román and J.D. Mello-Román and J.L. Vázquez Noguera and M. García-Torres and F. Divina and P.E. Sotomayor}, doi = {10.1109/ACCESS.2021.3131150 volume=9}, year = {2021}, date = {2021-01-01}, journal = {IEEE Access}, pages = {162487--162498}, publisher = {IEEE pubstate = published}, keywords = {feature selection, pattern recognition}, pubstate = {published}, tppubtype = {article} } |
F. Pietrapiana and J. M. Feria-Dominguez and A. Troncoso Applying wrapper-based variable selection techniques to predict MFIs profitability: evidence from Peru (Journal Article) Journal of Development Effectiveness, 2021. (Abstract | Links | BibTeX | Tags: feature selection) @article{JDE_Feria, title = {Applying wrapper-based variable selection techniques to predict MFIs profitability: evidence from Peru}, author = {F. Pietrapiana and J. M. Feria-Dominguez and A. Troncoso}, doi = {10.1080/19439342.2021.1884119}, year = {2021}, date = {2021-01-01}, journal = {Journal of Development Effectiveness}, abstract = {In this paper, we analyse the main factors explaining the profitability (ROA) of Microfinance Institutions (MFIs) in Peru from 2011 to 2107. We apply three wrapper techniques to a sample of 168 Peruvians MFIs and 69 attributes obtained from MIX Market database. After running the algorithms M5ʹ, k nearest neighbours (KNN) and Random Forest, we find that the M5ʹ algorithm provides the best fit for predicting ROA. Particularly, the key variable of the regression tree is the percentage of expenses over assets and, depending on its value, it is followed by net income after taxes and before donations, or profit margins.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } In this paper, we analyse the main factors explaining the profitability (ROA) of Microfinance Institutions (MFIs) in Peru from 2011 to 2107. We apply three wrapper techniques to a sample of 168 Peruvians MFIs and 69 attributes obtained from MIX Market database. After running the algorithms M5ʹ, k nearest neighbours (KNN) and Random Forest, we find that the M5ʹ algorithm provides the best fit for predicting ROA. Particularly, the key variable of the regression tree is the percentage of expenses over assets and, depending on its value, it is followed by net income after taxes and before donations, or profit margins. |
2019 |
G. Sosa-Cabrera and M. García-Torres and S. Gómez-Guerrero and C.E. Schaerer and F. Divina A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem (Journal Article) Information Sciences, 494 , pp. 1–20, 2019. (Abstract | Links | BibTeX | Tags: feature selection) @article{IS-2019, title = {A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem}, author = {G. Sosa-Cabrera and M. García-Torres and S. Gómez-Guerrero and C.E. Schaerer and F. Divina}, url = {https://www.sciencedirect.com/science/article/pii/S0020025519303603}, doi = {https://doi.org/10.1016/j.ins.2019.04.046}, year = {2019}, date = {2019-01-01}, journal = {Information Sciences}, volume = {494}, pages = {1--20}, abstract = {In this work we propose an extension of the Symmetrical Uncertainty (SU) measure in order to address the multivariate case, simultaneously acquiring the capability to detect possible correlations and interactions among features. This generalization, denoted Multivariate Symmetrical Uncertainty (MSU), is based on the concepts of Total Correlation (TC) and Mutual Information (MI) extended to the multivariate case. The generalized measure accounts for the total amount of dependency within a set of variables as a single monolithic quantity. Multivariate measures are usually biased due to several factors. To overcome this problem, a mathematical expression is proposed, based on the cardinality of all features, which can be used to calculate the number of samples needed to estimate the MSU without bias at a pre-specified significance level. Theoretical and experimental results on synthetic data show that the proposed sample size expression properly controls the bias. In addition, when the MSU is applied to feature selection on synthetic and real-world data, it has the advantage of adequately capturing linear and nonlinear correlations and interactions, and it can therefore be used as a new feature subset evaluation method.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } In this work we propose an extension of the Symmetrical Uncertainty (SU) measure in order to address the multivariate case, simultaneously acquiring the capability to detect possible correlations and interactions among features. This generalization, denoted Multivariate Symmetrical Uncertainty (MSU), is based on the concepts of Total Correlation (TC) and Mutual Information (MI) extended to the multivariate case. The generalized measure accounts for the total amount of dependency within a set of variables as a single monolithic quantity. Multivariate measures are usually biased due to several factors. To overcome this problem, a mathematical expression is proposed, based on the cardinality of all features, which can be used to calculate the number of samples needed to estimate the MSU without bias at a pre-specified significance level. Theoretical and experimental results on synthetic data show that the proposed sample size expression properly controls the bias. In addition, when the MSU is applied to feature selection on synthetic and real-world data, it has the advantage of adequately capturing linear and nonlinear correlations and interactions, and it can therefore be used as a new feature subset evaluation method. |
2018 |
G. Sosa-Cabrera and M. García-Torres and S. Gómez Guerrero and C.E. Schaerer and F. Divina Understanding a multivariate semi-metric in the search strategies for attributes subset selection (Conference) Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 2018. (Links | BibTeX | Tags: feature selection) @conference{Sosa2018b, title = {Understanding a multivariate semi-metric in the search strategies for attributes subset selection}, author = {G. Sosa-Cabrera and M. García-Torres and S. Gómez Guerrero and C.E. Schaerer and F. Divina}, url = {https://proceedings.sbmac.emnuvens.com.br/sbmac/article/view/2506}, year = {2018}, date = {2018-01-01}, booktitle = {Proceeding Series of the Brazilian Society of Computational and Applied Mathematics}, keywords = {feature selection}, pubstate = {published}, tppubtype = {conference} } |
2016 |
M. García-Torres and F. Gómez-Vela and B. Melián-Batista and J. Marcos Moreno-Vega High-dimensional feature selection via feature grouping: A Variable neighborhood Search approach (Journal Article) Information Sciences, 326 , pp. 102-118, 2016. (Links | BibTeX | Tags: feature selection) @article{IS:GT-2016, title = {High-dimensional feature selection via feature grouping: A Variable neighborhood Search approach}, author = {M. García-Torres and F. Gómez-Vela and B. Melián-Batista and J. Marcos Moreno-Vega}, url = {https://www.sciencedirect.com/science/article/pii/S0020025515005460}, doi = {10.1016/j.ins.2015.07.041}, year = {2016}, date = {2016-01-01}, journal = {Information Sciences}, volume = {326}, pages = {102-118}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } |
2013 |
M. García-Torres and R. Arma~nanzas and C. Bielza and P. Larra~naga Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data (Journal Article) Information Sciences, 222 , pp. 229-246, 2013. (Links | BibTeX | Tags: bioinformatics, feature selection) @article{IS:GT-2013, title = {Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data}, author = {M. García-Torres and R. Arma{~n}anzas and C. Bielza and P. Larra~naga}, url = {https://www.sciencedirect.com/science/article/pii/S0020025510006195}, doi = {10.1016/j.ins.2010.12.013}, year = {2013}, date = {2013-01-01}, journal = {Information Sciences}, volume = {222}, pages = {229-246}, keywords = {bioinformatics, feature selection}, pubstate = {published}, tppubtype = {article} } |
2012 |
R. Ruíz and J. Riquelme and J. Aguilar-Ruíz and M. García-Torres Fast feature selection aimed at high dimensional data via hybrid-sequential-ranked searches (Journal Article) Expert Systems with Applications, 39 (12), pp. 11094-11102, 2012. (Links | BibTeX | Tags: feature selection) @article{ESA:Rod-2012, title = {Fast feature selection aimed at high dimensional data via hybrid-sequential-ranked searches}, author = {R. Ruíz and J. Riquelme and J. Aguilar-Ruíz and M. García-Torres}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0957417412005842}, doi = {10.1016/j.eswa.2012.03.061}, year = {2012}, date = {2012-01-01}, journal = {Expert Systems with Applications}, volume = {39}, number = {12}, pages = {11094-11102}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } |
2006 |
F.~C. García-López and M. García-Torres and B. Melián-Batista and J.~A. Moreno Pérez and J.~M. Moreno-Vega Solving the Feature Selection Problem by a Parallel Scatter Search (Journal Article) European Journal of Operations Research, 169 (2), pp. 477-489, 2006. (Links | BibTeX | Tags: feature selection) @article{EJOR:GL-2006, title = {Solving the Feature Selection Problem by a Parallel Scatter Search}, author = {F.~C. García-López and M. García-Torres and B. Melián-Batista and J.~A. Moreno Pérez and J.~M. Moreno-Vega}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0377221704005491}, doi = {10.1016/j.ejor.2004.08.010}, year = {2006}, date = {2006-01-01}, journal = {European Journal of Operations Research}, volume = {169}, number = {2}, pages = {477-489}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } |
0000 |
M. García-Torres and F. Gómez-Vela and F. Divina and D.P. Pinto-Roa and J.L. Vázquez Noguera and J.C. Román Scatter search for high-dimensional feature selection using feature grouping (Conference) Proceedings of the Genetic and Evolutionary Computation Conference Companion, 0000. (Links | BibTeX | Tags: big data, feature selection, pattern recognition) @conference{garcia2021scatter, title = {Scatter search for high-dimensional feature selection using feature grouping}, author = {M. García-Torres and F. Gómez-Vela and F. Divina and D.P. Pinto-Roa and J.L. Vázquez Noguera and J.C. Román}, doi = {10.1145/3449726.3459481 pages=149--150}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, keywords = {big data, feature selection, pattern recognition}, pubstate = {published}, tppubtype = {conference} } |