A Comparative Study of the Stability of Filter based Feature Selection Algorithms

Abstract

Feature selection is an important step prior to classification stage of machine learning, pattern recognition and data mining problems for addressing the high dimensionality of the data. It removes irrelevant and redundant features which lead to simplify classification process and improve accuracy. Several feature selection algorithms have been proposed so far and quality of the selected feature subset varies from algorithm to algorithm. One of the measures for assessing the quality of a feature selection algorithm is its stability. Stability refers to the robustness of the selected feature set to small changes in the training set or set of various parameters of the algorithm. In this work, a comparative study of the stability of several well-known filter based feature selection algorithms, producing ranked feature sub set, has been done. Fifteen benchmark datasets from the UCI repository have been used for simulation experiments. Three types of stability measures, index-based, rank-based and weight based are used to evaluate the stability of feature selection algorithms. Simulation results demonstrate that for most of the datasets, JMD-based feature selection algorithm exhibits more stability irrespective of all types of stability measures. It is also observed that Relief shows the least stability.