Optimizing performance measures for feature selection

Mao, Q; Tsang, IWH

Optimizing performance measures for feature selection

Mao, Q Tsang, IWH

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 1170 - 1175
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Mao, Q	en_US
dc.contributor.author	Tsang, IWH https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 1170 - 1175	en_US
dc.identifier.isbn	9780769544083	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29435
dc.description.abstract	Feature selection with specific multivariate performance measures is the key to the success of many applications, such as information retrieval and bioinformatics. The existing feature selection methods are usually designed for classification error. In this paper, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. Extensive experiments on large-scale and high-dimensional real world datasets show that the proposed method outperforms l 1-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVM perfin terms of F 1-score. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2011.113	en_US
dc.title	Optimizing performance measures for feature selection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	en_US
dc.location.activity	Vancouver, Canada	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Feature selection with specific multivariate performance measures is the key to the success of many applications, such as information retrieval and bioinformatics. The existing feature selection methods are usually designed for classification error. In this paper, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. Extensive experiments on large-scale and high-dimensional real world datasets show that the proposed method outperforms l 1-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVM perfin terms of F 1-score. © 2011 IEEE.

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http://hdl.handle.net/10453/29435