Weighted nearest neighbor classification via maximizing classification consistency

Authors:
Pengfei Zhu;Qinghua Hu;Yongbin Yang
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

The nearest neighbor classification is a simple and effective technique for pattern recognition. The performance of this technique is known to be sensitive to the distance function used in classifying a test instance. In this paper, we propose a technique to learn sample weights via maximizing classification consistency. Experimental analysis shows that the distance trained in this way enlarges the classification consistency on several datasets and has a strong ability to tolerate noise. Moreover, the proposed approach has better performance than nearest neighbor classification and several state-of-the-art methods.