2013/02/07 by Fernando González, Fernando González‐Pérez, González, Fernando +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bioinformatics and Genomic Networks #Computational Engineering #Evolutionary Algorithms and Applications #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #and Science (cs.CE) #cs.CE #cs.LG #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1302.1733
12 pages, 6 Tables, 2 figures
arxiv created 2013/02/07 · openalex publication_date 2013/02/07 · arxiv updated 2013/02/08 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.