Comparative analysis of Bayes net classifier, naive Bayes classifier and combination of both classifiers using WEKA
Автор: Abhilasha Nakra, Manoj Duhan
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 3 Vol. 11, 2019 года.
Бесплатный доступ
Authors here tried to use the WEKA tool to evaluate the performance of various classifiers on a dataset to come out with the optimum classifier, for a particular application. A Classifier is an important part of any machine learning application. It is required to classify various classes and get to know whether the predicted class lies in the true class. There are various performance analysis measures to judge the efficiency of a classifier and there are many tools which provide oodles of classifiers. In the present investigation, Bayes Net, Naive Bayes and their combination have been implemented using WEKA. It has been concluded that the combination of Bayes Net and Naive Bayes provides the maximum classification efficiency out of these three classifiers. Such a hybridization approach will always motivate for combining different classifiers to get the best results.
Bayes Net, Naive Bayes, WEKA, Classifiers, Supervised, Unsupervised
Короткий адрес: https://sciup.org/15016343
IDR: 15016343 | DOI: 10.5815/ijitcs.2019.03.04
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