Adaptive Nearest Neighbor Classifier Based on Supervised Ellipsoid Clustering

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.

• Type: Conference
• Year: 2006
• Where: FSKD
• Authors: Guo-Jun Zhang, Ji-Xiang Du, De-Shuang Huang, Tat-Ming Lok, Michael R. Lyu
• Keywords: Adaptive Nearest Neighbor, FSKD 2006, Fuzzy Logic, Nearest Neighbor, Nearest Neighbor Classifiers