A HYBRID IMPROVING SCHEMA BETWEEN ID3 ALGORITHMS AND NAIVE BAYES CLASSIFIERS AND ITS APPLICATION TO THE POPULATION DATABASE OF BREAST CANCER

Niño Elias D., Nieto Wilson, Riascos Carlos. A Hybrid Improving Schema Between Id3 Algorithms And Naive Bayes Classifiers And Its Application To The Population Database Of Breast Cancer. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

Analyzed the principles of the ID3 algorithm, this creates rules based on the concepts of entropy and gain with prepared data set. On the other hand, naïve Bayes classifier, allow us to classify through of the prepared data set considered probabilistic evidence. We propose a hybrid schema based on the ID3 algorithm and the naïve Bayes classifier that let us to improve the accuracy in classification tasks. We believe that this may be useful in many types of applications, so this schema serve as a support tool for research as a way to make decisions. Finally, we use experiment to prove that the hybrid schema increase the accuracy being applied to population databases of breast cancer.

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