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Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables
Author(s) -
Wojciechowski T. J.
Publication year - 1987
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710290808
Subject(s) - k nearest neighbors algorithm , mathematics , limit (mathematics) , nonparametric statistics , best bin first , bayes' theorem , simple (philosophy) , cover (algebra) , naive bayes classifier , function (biology) , nearest neighbor graph , pattern recognition (psychology) , statistics , computer science , artificial intelligence , bayesian probability , support vector machine , mechanical engineering , mathematical analysis , philosophy , epistemology , evolutionary biology , engineering , biology
In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.