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A geometric approach to consistent classification
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DOI：10.1016/S0031-3203(99)00050-3.png)
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摘要
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A classifier is called consistent with respect to a given set of class-labeled points if it correctly classifies the set. We consider classifiers defined by unions of local separators (e.g., polytopes) and propose algorithms for consistent classifier reduction. The proposed approach yields a consistent reduction of the nearest-neighbor classifier, relating the expected classifier size to a local clustering property of the data and resolving unanswered questions raised by Hart (IEEE Trans. Inform. Theory IT-14(3) (1968)) with respect to the complexity of the condensed nearest neighbor method. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
classification
local separation
consistent reduction
nearest neighbor
condensed nearest neighbor
reduced nearest neighbor
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7.6
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1.3W
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