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.
The work described in this paper was sponsored in part by the National Science Council under Grant NSC 95-2221-E-142-002.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kuo, BC., Ho, HH., Li, CH., Chang, YY. (2006). A Novel Nearest Neighbor Classifier Based on Adaptive Nonparametric Separability. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_24
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DOI: https://doi.org/10.1007/11941439_24
Publisher Name: Springer, Berlin, Heidelberg
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