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.
Materials and 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.
Conclusion:
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.
An analysis of human-error factors using Fuzzy Integral-Measure model and natural languages.
