Performance Comparison of New Fast Weighted Naive Bayes Classifier with Other Bayes Classifiers

Abstract

Rapid development of the technology, along with the increasing amount of data, makes data analysis inconvenient. Nowadays, it is important that many processes can be recorded, stored and accessed in an electronic environment. As long as the data is not processed, it does not make any sense. Data mining is used to make the data meaningful. Data mining enables useful information to be reached by separating information from large-scale data. At the same time, it is the process of searching for the data by using software to make predictions about the future. In this study, a new fast weighted Bayesian Classifier is proposed, and its performance is compared with Naive Bayes Classifier and Weighted Naive Bayes Classifier, which is one of the data mining classification methods. Various data sets are used to obtain the results of the comparison. It is observed that the accuracy rate of the Fast Weighted Bayes Algorithm is better than Naive Bayes Classifier and it is faster than the Weighted Naive Bayes Classifier.