Multivariate feature ranking with high-dimensional data for classification tasks

Jiménez Barrionuevo, Fernando; Sanchez Carpena, G.; Palma Méndez, José Tomás; Miralles Pechuan, L.; Botia Blaya, J. A.

Publication:
Multivariate feature ranking with high-dimensional data for classification tasks

dc.contributor.author	Jiménez Barrionuevo, Fernando
dc.contributor.author	Sanchez Carpena, G.
dc.contributor.author	Palma Méndez, José Tomás
dc.contributor.author	Miralles Pechuan, L.
dc.contributor.author	Botia Blaya, J. A.
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2022-06-10T22:10:13Z
dc.date.available	2022-06-10T22:10:13Z
dc.date.issued	2022-06-08
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.abstract	In 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.format	application/pdf	es
dc.identifier.citation	IEEE Access
dc.identifier.citation	https://ieeeaccess.ieee.org/about-ieee-access/learn-more-about-ieee-access/
dc.identifier.doi	10.1109/ACCESS.2022.3180773
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10201/121146
dc.language	eng	es
dc.relation.isreferencedby	ED_IDENTRADA=1082
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Artificial intelligence	es
dc.subject	Feature Selection	es
dc.subject	Machine learning	es
dc.subject	rankers	es
dc.title	Multivariate feature ranking with high-dimensional data for classification tasks	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es