A hybrid feature selection method based on symmetrical uncertainty and support vector machine for high–dimensional data classification

Abstract

MicroRNA (miRNA) is a small, endogenous, and non-coding RNA that plays a critical regulatory role in various biological processes. Recently, researches based on microRNA expression profiles showed a new aspect of multiclass cancer classification. Due to the high dimensionality, however, classification of miRNA expression data contains several computational challenges. In this paper, we proposed a hybrid feature selection method for accurately classification of various cancer types based on miRNA expression data. Symmetrical uncertainty was employed as a filter part and support vector machine with best first search were used as a wrapper part. To validate the efficiency of the proposed method, we conducted several experiments on a real bead-based miRNA expression datasets and the results showed that our method can significantly improve the classification accuracy and outperformed the existing feature selection methods.