@inproceedings{a9bdde3ca3844465b7d85dfd7468caef,
title = "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.",
keywords = "Classification, Distance function, Receiver operating characteristics, Support vector machine",
author = "Shaoyi Zhang and Hossain, \{M. Maruf\} and Hassan, \{Md Rafiul\} and James Bailey and Kotagiri Ramamohanarao",
year = "2009",
doi = "10.1137/1.9781611972795.43",
language = "English",
isbn = "9780898716825",
series = "Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "493--504",
booktitle = "Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133",
address = "United States",
note = "9th SIAM International Conference on Data Mining, SDM 2009 ; Conference date: 30-04-2009 Through 02-05-2009",
}