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

A new approach for determining the prior probabilities in the classification problem by Bayesian... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Data Analysis and Classification Springer Journals

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

, Volume 11 (3) – May 25, 2016
15 pages

/lp/springer_journal/a-new-approach-for-determining-the-prior-probabilities-in-the-3CSpkncIFs
Publisher
Springer Berlin Heidelberg
Subject
Statistics; Statistical Theory and Methods; Data Mining and Knowledge Discovery
ISSN
1862-5347
eISSN
1862-5355
D.O.I.
10.1007/s11634-016-0253-y
Publisher site
See Article on Publisher Site

### 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.

### Journal

Advances in Data Analysis and ClassificationSpringer Journals

Published: May 25, 2016

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