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Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications


Omid Akbarzadeh, Mohammad Reza Khosravi* and Mehdi Shadloo-Jahromi   Pages 1 - 8 ( 8 )


Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing.

Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision.

Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes.

Conclusions: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.


Classification, Classifier Combination, Majority Vote, Integral Method, Naive Bayes


Department of Electronics and Telecommunications, Politecnico di Torino, Department of Electrical and Electronic Engineering, Shiraz University of Technology, Petropars Operation and Maintenance Company, Bushehr

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