Enhancing Forecasting Performance of Naive-Bayes Classifiers with Discretization Techniques
Ruxandra Petre ()
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Ruxandra Petre: University of Economic Studies, Bucharest, Romania
Database Systems Journal, 2015, vol. 6, issue 2, pages 24-30
During recent years, the amounts of data, collected and stored by organizations on a daily basis, have been growing constantly. These large volumes of data need to be analyzed, so organizations need innovative new solutions for extracting the significant information from these data. Such solutions are provided by data mining techniques, which apply advanced data analysis methods for discovering meaningful patterns within the raw data. In order to apply these techniques, such as NaĂŻve-Bayes classifier, data needs to be preprocessed and transformed, to increase the accuracy and efficiency of the algorithms and obtain the best results. This paper focuses on performing a comparative analysis of the forecasting performance obtained with the Naive-Bayes classifier on a dataset, by applying different data discretization methods opposed to running the algorithms on the initial dataset.
Keywords: Discretization; Naive-Bayes classifier; Data mining; Performance (search for similar items in EconPapers)
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Persistent link: http://EconPapers.repec.org/RePEc:aes:dbjour:v:6:y:2015:i:2:p:24-30
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