Kernel methods for heterogeneous feature selection
[摘要] This paper introduces two feature selection methods to deal with heterogeneous data that include continuous and categorical variables. We propose to plug a dedicated kernel that handles both kinds of variables into a Recursive Feature Elimination procedure using either a non-linear SVM or Multiple Kernel Learning. These methods are shown to offer state-of-the-art performances on a variety of high-dimensional classification tasks. (C) 2015 Elsevier B.V. All rights reserved.
[发布日期] 2015-12-02 [发布机构]
[效力级别] Proceedings Paper [学科分类]
[关键词] Heterogeneous feature selection;Kernel methods;Mixed data;Multiple kernel learning;Support vector machine;Recursive feature elimination [时效性]
