Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865573 | Neurocomputing | 2015 | 9 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jérôme Paul, Roberto D׳Ambrosio, Pierre Dupont,