The support feature machine for classifying with the least number of features

Authors:
Sascha Klement;Thomas Martinetz
Affiliations:
Institute for Neuro- and Bioinformatics, University of Lübeck;Institute for Neuro- and Bioinformatics, University of Lübeck
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

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Abstract

We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.