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 weight- ing. 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 pro- posed technique is tested on UCI data sets. Compared with Simba and LMFW, the proposed technique is effective and efficient.
['Wei Pan', 'Peijun Ma', 'Xiaohong Su']
Feature Weighting Algorithm Based on Margin and Linear Programming