Adaptive nearest neighbor classifier based on supervised ellipsoid clustering

Authors:
Guo-Jun Zhang;Ji-Xiang Du;De-Shuang Huang;Tat-Ming Lok;Michael R. Lyu
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei Anhui, China;Information Engineering Dept., The Chinese University of Hong Kong, Hong Kong;Computer Science & Engineering Dept., The Chinese University of Hong Kong, Hong Kong
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

Nearest neighbor classifier is a widely-used effective method for multi-class problems. However, it suffers from the problem of the curse of dimensionality in high dimensional space. To solve this problem, many adaptive nearest neighbor classifiers were proposed. In this paper, a locally adaptive nearest neighbor classification method based on supervised learning style which works well for the multi-classification problems is proposed. In this method, the ellipsoid clustering learning is applied to estimate an effective metric. This metric is then used in the K-NN classification. Finally, the experimental results show that it is an efficient and robust approach for multi-classification.