Semi-supervised feature selection via multiobjective optimization

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In previous work, we have shown that both unsupervised feature selection and the semi-supervised clustering problem can be usefully formulated as multiobjective optimization problems. In this paper, we discuss the logical extension of this prior work to cover the problem of semi-supervised feature selection. Our extensive experimental results provide evidence for the advantages of semi-supervised feature selection when both labelled and unlabelled data are available. Moreover, the particular effectiveness of a Pareto-based optimization approach can also be seen.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Number of pages8
ISBN (Print)0780394909, 9780780394902
Publication statusPublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CityVancouver, BC


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