Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

Background: The application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods. Purpose: We propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers. Conclusion: Statistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets. (C) 2014 Elsevier B.V. All rights reserved.