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ASME Press Select Proceedings
International Conference on Computer and Computer Intelligence (ICCCI 2011)
By
Yi Xie
Yi Xie
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ISBN:
9780791859926
No. of Pages:
740
Publisher:
ASME Press
Publication date:
2011

This article shows the importance of Bayesian classifiers for prediction in data mining, also as important components such as coverage and accuracy may improve the classification performance in themselves an analysis by performing a mathematical model such as Naive Bayes can be improved by adding coverage and precision. Finally, we believe that this improvement may be useful in many types of applications, so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment, also can also be used in previous applications to be improved in many ways.

Abstract
Key Words
1. Introduction
2. Bayes' Theorem
3. Na1ve Bayes Clasiffier
4. Bayessian Classifier Supported by Coverage and Accuracy
5. Evaluations and Comparations
6. Statistics Evaluations
7. Summaries
Acknowledgment
References
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