Abstract:
This paper aims to scale up the Naïve
Bayesian Classifier using Genetic and
Decision Tree for feature selection. The main
reason is to predict patient's breast cancer
result based on their diagnosis using this
scaled classifier. Naïve Bayes can suffer from
oversensitivity to redundant and/or irrelevant
attributes. Several researchers have
emphasized on the issue of redundant
attributes, as well as advantages of feature
selection for the Naïve Bayesian Classifier. In
this paper, Genetic algorithm is used to reduce
redundant attributes in feature selection, and
then apply Decision tree algorithm to find an
optimal set of feature weights that improve
classification accuracy. By combining genetic
algorithm with decision tree, and this method
enhance the Bayesian classification to
eliminate unnecessary features and produce
fast, accurate classifiers. Bayesian classifier
represents each class with a probabilistic
summary, and finds the most likely class for
each example it is asked to classify.
