Large margin principles for feature selection

Ran Gilad-Bachrach, Amir Navot, Naftali Tishby

Research output: Contribution to journalArticlepeer-review


In this paper we introduce a margin based feature selection criterion and apply it to measure the quality of sets of features. Using margins we devise novel selection algorithms for multi-class categorization problems and provide theoretical generalization bound. We also study the well known Relief algorithm and show that it resembles a gradient ascent over our margin criterion. We report promising results on various datasets.

Original languageEnglish
Pages (from-to)585-606
Number of pages22
JournalStudies in Fuzziness and Soft Computing
StatePublished - 2006
Externally publishedYes


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