Two-Step Feature Selection Methods for Selection of Very Few Features

The feature selection (FS) plays a important role in identification of the significant genes in bioinformatics and related fields. Additionally, it is frequently necessary step to avoid over-fitting and to reduce complexity and computational time. Wang et al [1] proposed new two stage feature selection method achieving excellent classification performance while selecting only few relevant genes. We present new feature selection methods, based on the idea of the Wang's paper, and analyze how the particular filter FS method, used in first stage, influence overall performance. The performance is analyzed by means of the FS stability and influence on the prediction performance. Our results indicate that the stability of FS is significantly affected by the type of FS used in the first stage, but the prediction performance is not so sensitive to the choice of FS in the first stage.