Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark

Authors:
Isabelle Guyon;Jiwen Li;Theodor Mader;Patrick A. Pletscher;Georg Schneider;Markus Uhr
Affiliations:
Pattern Analysis and Machine Learning Group, Institute of Computational Science, ETH Zurich, Universitaetstrasse 6, 8092 Zurich, Switzerland;Department of Informatics, University of Zurich, Binzmuhlestrasse 14, CH-8050 Zurich, Switzerland;Pattern Analysis and Machine Learning Group, Institute of Computational Science, ETH Zurich, Universitaetstrasse 6, 8092 Zurich, Switzerland;Pattern Analysis and Machine Learning Group, Institute of Computational Science, ETH Zurich, Universitaetstrasse 6, 8092 Zurich, Switzerland;Pattern Analysis and Machine Learning Group, Institute of Computational Science, ETH Zurich, Universitaetstrasse 6, 8092 Zurich, Switzerland;Pattern Analysis and Machine Learning Group, Institute of Computational Science, ETH Zurich, Universitaetstrasse 6, 8092 Zurich, Switzerland
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

We used the datasets of the NIPS 2003 challenge on feature selection as part of the practical work of an undergraduate course on feature extraction. The students were provided with a toolkit implemented in Matlab. Part of the course requirements was that they should outperform given baseline methods. The results were beyond expectations: the student matched or exceeded the performance of the best challenge entries and achieved very effective feature selection with simple methods. We make available to the community the results of this experiment and the corresponding teaching material [Anon. Feature extraction course, ETH WS 2005/2006. http://clopinet.com/isabelle/Projects/ETH]. These results also provide a new baseline for researchers in feature selection.