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.