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  Combining a Filter Method with SVMs

Lal, T., Chapelle, O., & Schölkopf, B. (2006). Combining a Filter Method with SVMs. In I. Guyon, S. Gunn, M. Nikravesh, & L. Zadeh (Eds.), Feature Extraction: Foundations and Applications (pp. 439-446). Berlin, Germany: Springer.

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 Creators:
Lal, TN1, 2, Author              
Chapelle, O1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].

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 Dates: 2006
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3011
DOI: 10.1007/978-3-540-35488-8_21
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Title: Feature Extraction: Foundations and Applications
Source Genre: Book
 Creator(s):
Guyon, I, Editor
Gunn, S, Editor
Nikravesh, M, Editor
Zadeh, LA, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 439 - 446 Identifier: ISBN: 978-3-540-35487-1

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Title: Studies in Fuzziness and Soft Computing
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Pages: - Volume / Issue: 207 Sequence Number: - Start / End Page: - Identifier: -