Abstract
Naive Bayes and Bayes net are critical classification method for data mining and have built up important software tools for the classification, description, and generalization of information. All classification algorithms are open sources, which are implemented in Java (C4.5 algorithms) for WEKA software tool. This paper exhibits the strategy for increasing the performance of Naive Bayes and Bayes net algorithms with supervised filter discretization. We have used the supervised filter discretization on these two classification algorithms and compared the result with and without discretization. The outcomes acquired from experiment showed significant improvement over the existing classification algorithms.
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Panwar, S.S., Raiwani, Y.P. (2020). Improving the Performance of Classification Algorithms with Supervised Filter Discretization Using WEKA on NSL-KDD Dataset. In: Siddiqui, N., Tauseef, S., Abbasi, S., Khan, F. (eds) Advances in Air Pollution Profiling and Control. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0954-4_16
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DOI: https://doi.org/10.1007/978-981-15-0954-4_16
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