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- W1858681147 abstract "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." @default.
- W1858681147 created "2016-06-24" @default.
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- W1858681147 date "2013-02-07" @default.
- W1858681147 modified "2023-09-27" @default.
- W1858681147 title "Feature Selection for Microarray Gene Expression Data using Simulated Annealing guided by the Multivariate Joint Entropy" @default.
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