Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms

Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomarkers for disease diagnosis and assessing drug efficiency. In this paper, we present an approach using a genetic algorithm for a feature subset selection problem that can be used in selecting the near optimum set of genes for classification of cancer data. In substantial improvement over existing methods, we classified cancer data with high accuracy with less features. Categories and Subject Descriptors I.2 [Computing Methodologies]: PATTERN RECOGNITION— Design Metodology—Feature Evolution and Selection; J.3 [Computer Applications]: LIFE AND MEDICAL SCIENCES—Biology and genetics General Terms Algorithms, Verification Keywords Biomarkers, colon cancer, prostate cancer, ovarian cancer, feature selection, classification, genetic algorithms.

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Alper Küçükural, Reyyan Yeniterzi, Süveyda Yeniterzi, Osman Ugur Sezerman
• Keywords: Cancer Data, Complex Disease Genes, GECCO 2007, Genetic Algorithms