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Semi-supervised feature selection under logistic I-RELIEF framework

  • University of Florida

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

We consider feature selection in the semi-supervised learning setting. This problem is rarely addressed in the literature. We propose a new algorithm as a natural extension of the recently developed Logistic I-RELIEF algorithm. The basic idea of the proposed algorithm is to modify the objective function of Logistic I-RELIEF to include the margins of unlabeled samples by following the large margin principle. Experimental results on artificial and benchmark datasets are presented to demonstrate the viability of the newly proposed method.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
StatePublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

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