On the Performance Assessment and Comparison of Features Selection Approaches

Abstract

In many supervised learning problems, feature selection techniques have become an apparent need in many applications. Feature selection significantly influences the classification accuracy rate and the quality of SVM model by reducing the number of feature, remove irrelevant and redundant features. In this paper, we evaluate the performance of twenty feature selection algorithms over four databases. The performance is conducted in term of: classification accuracy rate, Kuncheva’s Stability, Information Stability, SS Stability and SH Stability. To measure the feature selection algorithms, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate and the different stability.