A comparison between the wrapper and hybrid methods for feature selection on biology Omics datasets

Abstract

Classification problem in biological Omics data has gained in popularity in recent years. In consideration of the high dimension of the Omics dataset, the importance of feature selection technology becomes apparent. Feature selection can not only improve the classification accuracy but also avoid overfitting which is a common issue in machine learning. There are three main categories of feature selection methods: filter methods, wrapper methods, and hybrid methods. Yet it is difficult for researchers to choose among them. In this study, we conducted a comprehensive comparison for filter methods, wrapper methods, and hybrid methods. Specifically, we selected information gain as the filter method, genetic algorithm, and binary particle swarms optimization as the wrapper methods, IG-GA and IG-BPSO as the hybrid methods for comparison. The experimental results show that the IG-BPSO, a hybrid method, has the highest classification accuracy among these feature selection methods on three Omics datasets.