Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.12.098 | Neurocomputing |
Keywords | Field | DocType |
Heterogeneous feature selection,Kernel methods,Mixed data,Multiple kernel learning,Support vector machine,Recursive feature elimination | Graph kernel,Dimensionality reduction,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Multiple kernel learning,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | ISSN | Citations |
169 | 0925-2312 | 6 |
PageRank | References | Authors |
0.42 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Paul | 1 | 12 | 1.57 |
| Roberto D'Ambrosio | 2 | 13 | 2.30 |
| Pierre Dupont | 3 | 380 | 29.30 |