Neighborhood Counting Measure Metric and Minimum Risk Metric: An empirical comparison

Argentini, Andrea and Blanzieri, Enrico (2008) Neighborhood Counting Measure Metric and Minimum Risk Metric: An empirical comparison. UNSPECIFIED. (In Press)

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    Wang in a PAMI paper proposed Neighborhood Counting Measure (NCM) as a similarity measure for the k-nearest neighbors classification algorithm. In his paper, Wang mentioned Minimum Risk Metric (MRM) an earlier method based on the minimization of the risk of misclassification. However, Wang did not compare NCM with MRM because of its allegedly excessive computational load. In this letter, we empirically compare NCM against MRM on k-NN with k=1, 3, 5, 7 and 11 with decision taken with a voting scheme and k=21 with decision taken with a weighted voting scheme on the same datasets used by Wang. Our results shows that MRM outperforms NCM for most of the k values tested. Moreover, we show that the MRM computation is not so probihibitive as indicated by Wang. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA063 Problem solving
    Uncontrolled Keywords: Pattern Recognition, Machine Learning, k-Nearest Neighbors, distance measures, MRM, NCM
    Report Number: DISI-08-057
    Repository staff approval on: 20 Jul 2009

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