Abstract
Feature evaluation and selection is an important preprocessing step in classification and regression learning. As large quantity of irrelevant information is gathered, selecting the most informative features may help users to understand the task, and enhance the performance of the models. Margin has been widely accepted and used in evaluating feature quality these years. A collection of feature selection algorithms were developed using margin based loss functions and various search strategies. However, there is no comparative research conducted to study the effectiveness of these algorithms. In this work, we compare 14 margin based feature selections from the viewpoints of reduction capability, classification performance of reduced data and robustness, where four margin based loss functions and three search strategies are considered. Moreover, we also compare these techniques with two well-known margin based feature selection algorithms ReliefF and Simba. The derived conclusions give some guidelines for selecting features in practical applications.
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