Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification

Wu, J; Cai, ZH; Ao, S

Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification

Wu, J Cai, ZH Ao, S

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Publication Type:: Journal Article
Citation:: International Journal of Computer Applications in Technology, 2012, 43 (4), pp. 378 - 384
Issue Date:: 2012-06-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Cai, ZH	en_US
dc.contributor.author	Ao, S	en_US
dc.date.issued	2012-06-01	en_US
dc.identifier.citation	International Journal of Computer Applications in Technology, 2012, 43 (4), pp. 378 - 384	en_US
dc.identifier.issn	0952-8091	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121533
dc.description.abstract	K-nearest-neighbour (KNN) as an important classification method has been widely used in data mining. However, the class probability estimation, the neighbourhood size and the type of distance function confronting KNN may affect its classification accuracy. Many researchers have been focused on improving the accuracy of KNN via distance weighted, attribute weighted, and dynamic selected methods etc. In this paper, we firstly reviewed some improved algorithms of KNN in three categories mentioned above. Then, we singled out an improved algorithm called dynamic KNN with distance and attribute weighted, simply DKNDAW. We experimentally tested our new algorithm in Weka system. In our experiment, we compared it to KNN, WAKNN, KNNDW, KNNDAW, and DKNN. The experimental results show that DKNDAW significantly outperforms other algorithms in terms of the classification accuracy. Besides, how to learn a weighted DKNDAW with accurate ranking from data, or more precisely, different attribute weighted method of DKNDAW can produce accurate ranking. We explore various methods: the gain ratio method, the correlation-based feature selection method, and the decision tree-based method. We concluded that the gain ratio method is more suitable for our improved KNN algorithm DKNDAW. Copyright © 2012 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Computer Applications in Technology	en_US
dc.relation.isbasedon	10.1504/IJCAT.2012.047164	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	43	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	1099 Other Technology	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	43	en_US

Abstract:

K-nearest-neighbour (KNN) as an important classification method has been widely used in data mining. However, the class probability estimation, the neighbourhood size and the type of distance function confronting KNN may affect its classification accuracy. Many researchers have been focused on improving the accuracy of KNN via distance weighted, attribute weighted, and dynamic selected methods etc. In this paper, we firstly reviewed some improved algorithms of KNN in three categories mentioned above. Then, we singled out an improved algorithm called dynamic KNN with distance and attribute weighted, simply DKNDAW. We experimentally tested our new algorithm in Weka system. In our experiment, we compared it to KNN, WAKNN, KNNDW, KNNDAW, and DKNN. The experimental results show that DKNDAW significantly outperforms other algorithms in terms of the classification accuracy. Besides, how to learn a weighted DKNDAW with accurate ranking from data, or more precisely, different attribute weighted method of DKNDAW can produce accurate ranking. We explore various methods: the gain ratio method, the correlation-based feature selection method, and the decision tree-based method. We concluded that the gain ratio method is more suitable for our improved KNN algorithm DKNDAW. Copyright © 2012 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/121533