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Increasing the Accuracy of Incremental Naive Bayes Classifier Increasing the Accuracy of Incremental Naive Bayes Classifier

Sotiris Kotsiantis
International Journal of Control, Automation, and Systems, vol. 11, no. 1, pp.159-166, 2013

Abstract : Along with the increase of data and information, incremental learning ability turns out to be more and more important for machine learning approaches. The online algorithms try not to remember irrelevant information instead of synthesizing all available information (as opposed to classic batch learning algorithms). In this study, we attempted to increase the prediction accuracy of an incremental version of Naive Bayes model by integrating instance based learning. We performed a large-scale comparison of the proposed method with other state-of-the-art algorithms on several datasets and the proposed method produce better accuracy in most cases.

Keyword : Concept drift, incremental machine learning, online learning.

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