Classification using semiparametric mixtures |
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Authors: | Yong Wang Xuxu Wang |
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Affiliation: | Department of Statistics, University of Auckland, Auckland, New Zealand |
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Abstract: | A new density-based classification method that uses semiparametric mixtures is proposed. Like other density-based classifiers, it first estimates the probability density function for the observations in each class, with a semiparametric mixture, and then classifies a new observation by the highest posterior probability. By making a proper use of a multivariate nonparametric density estimator that has been developed recently, it is able to produce adaptively smooth and complicated decision boundaries in a high-dimensional space and can thus work well in such cases. Issues specific to classification are studied and discussed. Numerical studies using simulated and real-world data show that the new classifier performs very well as compared with other commonly used classification methods. |
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Keywords: | Classification mixture model nonparametric mixture semiparametric mixture density estimation decision boundary discriminant analysis |
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