Feature Weighted SVMs Using Receiver Operating Characteristics

Abstract

Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used to map the input space into a high dimensional feature space. However, it can perform rather poorly when there are too many dimensions (e.g. for gene expression data) or when there is a lot of noise. In this paper, we investigate the suitability of using a new feature weighting scheme for SVM kernel functions, based on receiver operating characteristics (ROC). This strategy is clean, simple and surprisingly effective. We experimentally demonstrate that it can significantly and substantially boost classification performance, across a range of datasets.