An Adaptive Nearest Neighbor Classifier Based on Nonparametric Separability
Hsin-Hua Ho, Cheng‐Hsuan Li, Bor‐Chen Kuo · 2007
A k-nearest-neighbor classifier expects the class conditional probabilities to be locally constant. In this paper, we use the local separability based on nonparametric weighted feature extraction 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.