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Activity Number:
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535
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305668 |
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Title:
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Empirical Bayes Decision Rule for Classification Using Unsupervised Learning
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Author(s):
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Shui-Ching Chang*+ and Tze-Fen Li
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Companies:
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The Oversea Chinese Institute of Technology and National Chung Hsing University
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Address:
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Department of Business Administration, Taichung, 407, Taiwan
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Keywords:
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Bayes decision rule ; classification ; empirical Bayes ; quality control ; stochastic approximation ; unsupervised learning
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Abstract:
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A set of unlabelled items is used to establish a decision rule to classify defective items. We suppose the lifetime of an item has a Weibull distribution. The Bayes decision rule with estimated parameters is an empirical Bayes (EB) decision rule. A stochastic approximation procedure using the set of unidentified samples is established to estimate these unknown parameters. When the size of unlabelled items increases, the estimates computed by the procedure converge to the real parameters and gradually adapt our EB decision rule to be a better classifier until it becomes the Bayes decision rule. The results of a Monte Carlo simulation study are presented to demonstrate the convergence of the correct classification rates made by the EB decision rule to the highest correct classification rates given by the Bayes decision rule.
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