Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25724
Title: Feature evaluation and selection based on neighborhood soft margin
Authors: Hu, Q
Che, X
Zhang, L 
Yu, D
Keywords: Feature evaluation
Feature selection
Margin
Neighborhood
Issue Date: 2010
Publisher: Elsevier
Source: Neurocomputing, 2010, v. 73, no. 10-12, p. 2114-2124 How to cite?
Journal: Neurocomputing 
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.
URI: http://hdl.handle.net/10397/25724
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2010.02.007
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