Liang, Jianning and Yang, Su and Winstanley, Adam C. (2008) Invariant optimal feature selection: A distance discriminant and feature ranking based solution. Pattern Recognition, 41 (5). pp. 1429-1439. ISSN 0031-3203

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Abstract

The goal of feature selection is to find the optimal subset consisting of m features chosen from the total n features. One critical problem for many feature selection methods is that an exhaustive search strategy has to be applied to seek the best subset among all the possible View the MathML sourcenm feature subsets, which usually results in a considerably high computational complexity. The alternative suboptimal feature selection methods provide more practical solutions in terms of computational complexity but they cannot promise that the finally selected feature subset is globally optimal. We propose a new feature selection algorithm based on a distance discriminant (FSDD), which not only solves the problem of the high computational costs but also overcomes the drawbacks of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. The proposed method is invariant to the linear transformation of data when a diagonal transformation matrix is applied. FSDD was compared with ReliefF and mrmrMID based on mutual information on 8 data sets. The experiment results show that FSDD outperforms the other two methods and is highly efficient.

Item Type:	Article
Keywords:	Optimal feature selection; Distance discriminant; Feature ranking;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	6924
Identification Number:	https://doi.org/10.1016/j.patcog.2007.10.018
Depositing User:	Dr. Adam Winstanley
Date Deposited:	26 Jan 2016 12:57
Journal or Publication Title:	Pattern Recognition
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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