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dc.contributor.authorJimenez Barrionuevo, F.-
dc.contributor.authorSanchez Carpena, G.-
dc.contributor.authorPalma Mendez, J. T.-
dc.contributor.authorMiralles Pechuan, L.-
dc.contributor.authorBotia Blaya, J. A.-
dc.identifier.citationIEEE Accesses_ES
dc.description©2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted Manuscript version of a Published Work that appeared in final form in IEEE Access. To access the final edited and published work see DOI 10.1109/ACCESS.2022.3180773-
dc.description.abstractIn many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which havebeen applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicable.-
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International-
dc.subjectArtificial intelligencees_ES
dc.subjectFeature Selectiones_ES
dc.subjectMachine learninges_ES
dc.titleMultivariate feature ranking with high-dimensional data for classification taskses_ES
Aparece en las colecciones:Artículos: Ingeniería de la Información y las Comunicaciones

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