Performance comparison of new fast weighted Naïve bayes classifier with other bayes classifiers - Aksoy, Gamzepelin; Karabatak, Murat (Institute of Electrical and Electronics Engineers Inc., 2019)
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 Naïve Bayes Classifier and Weighted Naïve 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 Naïve Bayes Classifier and it is faster than the Weighted Naïve Bayes Classifier. © 2019 IEEE.
7th International Symposium on Digital Forensics and Security, ISDFS 2019 -- 10 June 2019 through 12 June 2019 -- Barcelos -- 149395
Keywords: Bayes methods; Classification algorithms; Data mining; Weighted Naïve Bayes