Abstract
Feature selection is an important task in machine learning. In this work, we design a robust algorithm for optimal feature subset selection. We present a global optimization technique for feature weighting. Margin induced loss functions are introduced to evaluate features, and we employs linear programming to search the optimal solution. The derived weights are combined with the nearest neighbor rule. The proposed technique is tested on UCI data sets. Compared with Simba and LMFW, the proposed technique is effective and efficient.
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Pan, W., Ma, P., Su, X. (2012). Feature Weighting Algorithm Based on Margin and Linear Programming. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_46
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DOI: https://doi.org/10.1007/978-3-642-32115-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32114-6
Online ISBN: 978-3-642-32115-3
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