A new approach for determining the prior probabilities in the classification problem by Bayesian method

Thao Nguyen-Trang () and Tai Vo- Van
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Thao Nguyen-Trang: Ton Duc Thang University
Tai Vo- Van: Can Tho University

Advances in Data Analysis and Classification, 2017, vol. 11, issue 3, No 10, 629-643

Abstract: Abstract In this article, we suggest a new algorithm to identify the prior probabilities for classification problem by Bayesian method. The prior probabilities are determined by combining the information of populations in training set and the new observations through fuzzy clustering method (FCM) instead of using uniform distribution or the ratio of sample or Laplace method as the existing ones. We next combine the determined prior probabilities and the estimated likelihood functions to classify the new object. In practice, calculations are performed by Matlab procedures. The proposed algorithm is tested by the three numerical examples including bench mark and real data sets. The results show that the new approach is reasonable and gives more efficient than existing ones.

Keywords: Classification; Bayes error; BayesC; Prior probability; 62H30; 68T10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-016-0253-y

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