Comparative Analysis of Breast Cancer Data Using C4.5 Classifier and Naive Bayes Algorithm

This study is aimed to identify the breast cancer using data mining classification methods. The dataset named Wisconsin Breast Cancer Database (WBCD) are obtained from university of California Irvine (UCI) respiratory and The Wisconsin Madison University. By using this dataset a comparison of two different classifiers that can be used machine learning algorithms, namely the Naïve Bayes algorithm and C4.5 Classification algorithm. Bayesian classifiers are the statistical classifiers. Bayesian classifiers predicts class membership probabilities such as the probability that a given tuple belongs to a particular class. The decision trees that are generated by using C4.5 classifier can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. In order to measure the performance, the holdout method is the simplest kind of cross validation. The data set can be separated into two sets, called the training set and the testing set. The function approximator allows to fit a function using the training set. The prediction of the output values for the data in the testing set is done by the function approximator (it has never seen these output values before).