A novel nearest neighbor classifier based on adaptive nonparametric separability

Authors:
Bor-Chen Kuo;Hsin-Hua Ho;Cheng-Hsuan Li;Ya-Yuan Chang
Affiliations:
Department of Graduate School of Educational Measurement and Statistics, National Taichung University, Taiwan, Taichung, Taiwan, R.O.C.;Department of Graduate School of Educational Measurement and Statistics, National Taichung University, Taiwan, Taichung, Taiwan, R.O.C.;Department of Applied Mathematics, Feng Chia University, Taiwan, Taichung, Taiwan, R.O.C.;Department of Graduate School of Educational Measurement and Statistics, National Taichung University, Taiwan, Taichung, Taiwan, R.O.C.
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 3
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation

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Abstract

A k-nearest-neighbor classifier expects the class conditional probabilities to be locally constant. In this paper, we use the local separability based on NWFE criterion to establish an effective metric for computing a new neighborhood. For each test pattern, the modified neighborhood shrinks in the direction with high separability around this pattern and extends further in the other direction. This new neighborhood can often provide improvement in classification performance. Therefore, any neighborhood-based classifier can be employed by using the modified neighborhood. Then the class conditional probabilities tend to be more homogeneous in the modified neighborhood.