Article ID Journal Published Year Pages File Type
6865573 Neurocomputing 2015 9 Pages PDF
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.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
Authors
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