2026-04-13T00:56:56Zhttps://recercat.cat/oai/requestoai:recercat.cat:2117/967072025-07-17T03:07:18Zcom_2072_1033col_2072_452950
RECERCAT
author
González Navarro, Félix Fernando
author
Belanche Muñoz, Luis Antonio
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)
Open Access
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
External research report