Embedded Gene Selection for Imbalanced Microarray Data Analysis

Abstract

Most of microarray data sets are imbalanced, i.e. the number of positive examples is much less than that of negative, which will hurt performance of classifiers when it is used for tumor classification. Though it is critical, few previous works paid attention to this problem. Here we propose embedded gene selection with two algorithms i.e. EGSEE (Embedded Gene Selection for EasyEnsemble) and EGSIEE (Embedded Gene Selection for Individuals of EasyEnsemble) to treat this problem and improve generalization performance of the EasyEnsemble classifier. Experimental results on several microarray data sets show that compared with the previous two filter feature selection methods, EGSEE and EGSIEE obtain better performance.