Combination of Feature Selection Methods for the Effective Classification of Microarray Gene Expression Data

Gene selection from microarray gene expression data is very difficult due to the large dimensionality of the data. The number of samples in the microarray data set is very small compared to the number of genes as features. To reduce dimensionality, selection of significant genes is necessary. An effective method of gene feature selection helps in dimensionality reduction and improves the performance of the sample classification. In this work, we have examined if combination of feature selection methods can improve the performance of classification algorithms. We propose two methods of combination of feature selection techniques. Experimental results suggest that appropriate combination of filter gene selection methods is more effective than individual techniques for microarray data classification. We have compared our combination methods using different learning algorithms.