A hybrid method of feature selection for microarray gene expression data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved, available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. A reliable selection method for genes relevant for sample classification is needed in order to speed up the processing rate, decrease the predictive error rate, and to avoid incomprehensibility due to the large number of genes investigated. In this study, we combined information gain and an improved binary particle swarm optimization as a hybrid method to implement feature selection, and the K-nearest neighbor (K-NN) method serves as an evaluator for gene expression data classification problems. Experimental results show that this method effectively simplifies feature selection and reduces the total number of features needed. The classification accuracy obtained by the proposed method has the highest classification accuracy in 6 gene expression data test problems, and is comparative to the classification accuracy, as compared to the best results previously published.