A Study On Similarity Measures For Feature Selection Method OFFSS

Feature subset selection has been playing a very important role in machine learning, data-mining and pattern recognition. OFFSS (Optimal Fuzzy-valued Feature Subset Selection) is a new fuzzy-valued feature selection method that selects an optimal feature subset from the feature space by considering both the overall overlapping degree between two classes of examples. In comparison with other methods such as OFEI, FQI and MIFS, OFFSS has no significant difference in training and testing accuracy of the selected feature subset but has much less computational complexity. Since the overlapping degree of OFFSS algorithm is dependent of a similarity measure so that different similarity measures may lead to different feature subsets to be selected. In this paper, we study the impact of similarity measures on the results of OFFSS for the same dataset. Based on triangular membership functions, we demonstrate the relationship among overlapping degree, feature subset, and classification accuracy that are produced by OFFSS using three classes of similarity measures respectively. And then one similarity measure is found for selecting less number of features.