Feature Selection for Microarray Gene Expression Data using Simulated Annealing guided by the Multivariate Joint Entropy

TL;DR

This paper introduces a novel information-theoretic method for feature selection in microarray gene expression data, utilizing a new multivariate joint entropy calculation and a simulated annealing algorithm to identify relevant gene subsets efficiently.

Contribution

It presents a new low-complexity multivariate joint entropy measure and a specialized simulated annealing algorithm for effective gene subset selection in microarray data.

Findings

High classification performance achieved

Selected gene subsets are biologically meaningful

Algorithm demonstrates low computational complexity

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.