Semi-supervised feature selection via multiobjective optimization

Julia Handl; Joshua Knowles

doi:10.1109/ijcnn.2006.247330

Semi-supervised feature selection via multiobjective optimization

Julia Handl^*, Joshua Knowles

^*Corresponding author for this work

Department of Chemistry

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

Abstract

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 language	English
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Publisher	IEEE
Pages	3319-3326
Number of pages	8
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/ijcnn.2006.247330
Publication status	Published - 2006
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

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

Conference

Conference	International Joint Conference on Neural Networks 2006, IJCNN '06
Country/Territory	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

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10.1109/ijcnn.2006.247330

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@inproceedings{18541597afbe43edaf81c89ba45ae9fc,

title = "Semi-supervised feature selection via multiobjective optimization",

abstract = "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.",

author = "Julia Handl and Joshua Knowles",

year = "2006",

doi = "10.1109/ijcnn.2006.247330",

language = "English",

isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

publisher = "IEEE",

pages = "3319--3326",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

address = "United States",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

}

Handl, J & Knowles, J 2006, Semi-supervised feature selection via multiobjective optimization. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716552, IEEE International Conference on Neural Networks - Conference Proceedings, IEEE, pp. 3319-3326, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 16/07/06. https://doi.org/10.1109/ijcnn.2006.247330

Semi-supervised feature selection via multiobjective optimization. / Handl, Julia ; Knowles, Joshua.
International Joint Conference on Neural Networks 2006, IJCNN '06. IEEE, 2006. p. 3319-3326 1716552 (IEEE International Conference on Neural Networks - Conference Proceedings).

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

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AB - 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.

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SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

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ER -

Semi-supervised feature selection via multiobjective optimization

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