2026-04-13T16:51:58Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/967072025-07-17T03:07:18Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc González Navarro, Félix Fernando author Belanche Muñoz, Luis Antonio author 2013 In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes. Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica Feature selection Microarray gene expression data Multivariate joint entropy Simulated annealing Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy