A modified k-nearest neighbor classifier to deal with unbalanced classes

Alsukker, A; Al-Ani, A; Atiya, A

A modified k-nearest neighbor classifier to deal with unbalanced classes

Alsukker, A Al-Ani, A

Atiya, A

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Publication Type:: Conference Proceeding
Citation:: IJCCI 2009 - International Joint Conference on Computational Intelligence, Proceedings, 2009, pp. 408 - 413
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Alsukker, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Atiya, A	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	IJCCI 2009 - International Joint Conference on Computational Intelligence, Proceedings, 2009, pp. 408 - 413	en_US
dc.identifier.isbn	9789896740146	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12646
dc.description.abstract	We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the proposed algorithm managed to achieve a uniformly good performance.	en_US
dc.relation.ispartof	IJCCI 2009 - International Joint Conference on Computational Intelligence, Proceedings	en_US
dc.title	A modified k-nearest neighbor classifier to deal with unbalanced classes	en_US
dc.type	Conference Proceeding
utslib.for	1004 Medical Biotechnology	en_US
dc.location.activity	Madeira, Portugal	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the proposed algorithm managed to achieve a uniformly good performance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/12646