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Kernel methods for heterogeneous feature selection
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DOI：10.1016/j.neucom.2014.12.098.png)
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Abstract
En 中文
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.
Keywords:
Heterogeneous feature selection
Kernel methods
Mixed data
Multiple kernel learning
Support vector machine
Recursive feature elimination
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