A Relief Based Feature Subset Selection Method

Abstract

Feature selection methods are used as a preliminary step in different areas of machine learning. Feature selection usually involves ranking the features or extracting a subset of features from the original dataset. Among various types of feature selection methods, distance-based methods are popular for their simplicity and better accuracy. Moreover, they can capture the interaction among the features for a particular application. However, it is difficult to decide the appropriate feature subset for better accuracy from the ranked feature set. To solve this problem, in this paper we propose Relief based Feature Subset Selection (RFSS), a method to capture more interactive and relevant feature subset for obtaining better accuracy. Experimental result on 16 benchmark datasets demonstrates that the proposed method performs better in comparison to the state-of-the-art methods. DUJASE Vol. 6 (2) 7-13, 2021 (July)

Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System

“…Authors in [41] proposed a so-called relief algorithm to select features. This algorithm uses a statistical method and avoids a heuristic search.…”