  Gene expression data is widely used in disease analysis and cancer diagnosis.
However, since gene expression data could contain thousands of genes
simultaneously, successful microarray classification is rather difficult.
Feature selection is an important pre-treatment for any classification process.
Selecting a useful gene subset as a classifier not only decreases the
computational time and cost, but also increases classification accuracy. In
this study, we applied the information gain method as a filter approach, and an
improved binary particle swarm optimization as a wrapper approach to implement
feature selection; selected gene subsets were used to evaluate the performance
of classification. Experimental results show that by employing the proposed
method fewer gene subsets needed to be selected and better classification
accuracy could be obtained.
