Feature evaluation and selection based on neighborhood soft margin

Authors:
Qinghua Hu;Xunjian Che;Lei Zhang;Daren Yu
Affiliations:
Harbin Institute of Technology, Harbin, China and Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Harbin Institute of Technology, Harbin, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Harbin Institute of Technology, Harbin, China
Venue:
Neurocomputing
Year:
2010

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Abstract

Feature selection is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. In this work, we propose a new concept of neighborhood margin and neighborhood soft margin to measure the minimal distance between different classes. We use the criterion of neighborhood soft margin to evaluate the quality of candidate features and construct a forward greedy algorithm for feature selection. We conduct this technique on eight classification learning tasks and some cancer recognition tasks. Compared with the raw data and other feature selection algorithms, the proposed technique is effective in most of the cases.