JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 12    Date : December 2008

A Novel Feature Selection Algorithm Based on Hypothesis-Margin
Ming Yang, Fei Wang, and Ping Yang
Page(s): 27-34
Full Text:
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Abstract
Iterative search margin based algorithm(Simba) has been proven effective for feature selection.
However, it still has the following disadvantages: (1) the previously proposed model still lacks
enough robust to noises; and (2) the given model does not use any global information, in this way
some useful discrimination information may be lost and the convergence speed is also influenced
in some cases. In this paper, by incorporating global information, a novel margin based feature
selection framework is introduced. According to the newly designed model, an improved margin
based feature selection algorithm(Isimba) is proposed. By effectively adjusting the contribution of
the global information, Isimba can efficiently reduce the computational cost and at the same time
obtain more effective feature subsets as compared to Simba. The experiments on 6 artificial and 8
real-life benchmark datasets show that Isimba is effective and efficient.

Index Terms
Feature selection, Dimensionality reduction, Hypothesis-margin, Margin