Open AccessProceedings Article
Consistent Classification, Firm and Soft
Yoram Baram
- 03 Dec 1996
- Vol. 9, pp 326-332
TL;DR: This work considers classifiers defined by unions of local separators and proposes algorithms for consistent classifier reduction, which yields a consistent reduction of the nearest neighbor classifier, which performs "firm" classification.
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Abstract: 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 and propose algorithms for consistent classifier reduction. The expected complexities of the proposed algorithms are derived along with the expected classifier sizes. In particular, the proposed approach yields a consistent reduction of the nearest neighbor classifier, which performs "firm" classification, assigning each new object to a class, regardless of the data structure. The proposed reduction method suggests a notion of "soft" classification, allowing for indecision with respect to objects which are insufficiently or ambiguously supported by the data. The performances of the proposed classifiers in predicting stock behavior are compared to that achieved by the nearest neighbor method.
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References
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