GA-NobRFE-SVM: A New Algorithm for Classification of Unbalanced Gene Data

Abstract

As an important branch of bioinformatics, gene microarray data analysis has become one of the important frontier fields in life sciences. Because of the high cost of microarray experiment, gene expression profile data shows the characteristics of small samples size, high dimensionality and category imbalance between samples. In this case, the traditional feature selection method is difficult to obtain good results. this paper proposes unbiased SVM-RFE (NobSVM-RFE). Compared with traditional feature selection algorithm, NobSVM-RFE algorithm can obtain better feature subset and reduce computation cost. Combining GASMOTE with NobSVM-RFE, this paper proposes a three-stage algorithm GA-NobRFE-SVM, which includes balancing algorithm, feature selection algorithm and classifier. The experimental results show that GA-NobRFE-SVM can effectively improve the classification performance of unbalanced gene data.