Feature selection based on sparse representation with the measures of classification error rate and complexity of boundary

Feature subset selection plays an important role in pattern recognition, classification systems, and data mining. We study how to select good features by optimizing multivariate performance measures based on sparse representation. In this paper, we first propose a novel feature evaluation measure, called counting region covering (CRC), for estimating classification complexity in different feature subspaces. Then, we present a unified feature selection framework by optimizing the classification error rate and complexity of boundary simultaneously. This allows us to select a compact set of superior features at high classification accuracy. Finally, we discuss the influence of weighting factors in feature selection framework. We perform experimental comparison of our algorithm and other methods using a support vector machine classifier and five different data sets (iris, wine, sonar, iono, and waveform). Experimental results on five real-world datasets demonstrate the effectiveness of our algorithm.