A novel prototype reduction method for the K-nearest neighbor algorithm with K ≥ 1

Yang, T; Cao, L; Zhang, C

A novel prototype reduction method for the K-nearest neighbor algorithm with K ≥ 1

Yang, T Cao, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 89 - 100
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 89 - 100	en_US
dc.identifier.isbn	3642136710	en_US
dc.identifier.isbn	9783642136719	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16135
dc.description.abstract	In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage requirement and enhancing the online speed while retaining the same level of accuracy for a K-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Unlike the classical methods PW, LPD and WDNN which can only work with K = 1, our developed algorithm can work with K ≥ 1. This flexibility allows our learning algorithm to have superior classification accuracy and noise robustness. The proposed approach is assessed through experiments with twenty real world benchmark data sets. In all these experiments, the proposed approach shows it can dramatically reduce the storage requirement and online time for KNN while having equal or better accuracy than KNN, and it also shows comparable results to several prototype reduction methods proposed in literature. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-13672-6_10	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A novel prototype reduction method for the K-nearest neighbor algorithm with K ≥ 1	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	6119 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hyderabad, India	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	6119 LNAI	en_US

Abstract:

In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage requirement and enhancing the online speed while retaining the same level of accuracy for a K-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Unlike the classical methods PW, LPD and WDNN which can only work with K = 1, our developed algorithm can work with K ≥ 1. This flexibility allows our learning algorithm to have superior classification accuracy and noise robustness. The proposed approach is assessed through experiments with twenty real world benchmark data sets. In all these experiments, the proposed approach shows it can dramatically reduce the storage requirement and online time for KNN while having equal or better accuracy than KNN, and it also shows comparable results to several prototype reduction methods proposed in literature. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/16135