Multiscale classification using nearest neighbor density estimates

Ghosh, A. K. ; Chaudhuri, P. ; Murthy, C. A. (2006) Multiscale classification using nearest neighbor density estimates IEEE Transactions on Systems, Man and Cybernatics, 36 (5). pp. 1139-1148. ISSN 1083-4419

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...

Related URL: http://dx.doi.org/10.1109/TSMCB.2006.873186

Abstract

Density estimates based on k-nearest neighbors have useful applications in nonparametric discriminant analysis. In classification problems, optimal values of k are usually estimated by minimizing the cross-validated misclassification rates. However, these cross-validation techniques allow only one value of k for each population density estimate, while in a classification problem, the optimum value of k for a class may also depend on its competing population densities. Further, it is computationally difficult to minimize the cross-validated error rate when there are several competing populations. Moreover, in addition to depending on the entire training data set, a good choice of k should also depend on the specific observation to be classified. Therefore, instead of using a single value of k for each population density estimate, it is more useful in practice to consider the results for multiple values of k to arrive at the final decision. This paper presents one such approach along with a graphical device, which gives more information about classification results for various choices of k and the related statistical uncertainties present there. The utility of this proposed methodology has been illustrated using some benchmark data sets.

Item Type:Article
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ID Code:74637
Deposited On:17 Dec 2011 10:37
Last Modified:17 Dec 2011 10:37

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