Performance Analysis of Naive Bayes and J 48 Classification Algorithm for Data Classification

Classification is an important data mining technique with broad applications to classify the various kinds of data used in nearly every field of our life. Classification is used to classify the item according to the features of the item with respect to the predefined set of classes. This paper put a light on performance evaluation based on the correct and incorrect instances of data classification using Naïve Bayes and J48 classification algorithm. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. The paper sets out to make comparative evaluation of classifiers NAIVE BAYES AND J48 in the context of bank dataset to maximize true positive rate and minimize false positive rate of defaulters rather than achieving only higher classification accuracy using WEKA tool. The experiments results shown in this paper are about classification accuracy, sensitivity and specificity. The results in the paper on this dataset also show that the efficiency and accuracy of j48 is better than that of |Naïve bayes.