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Performance Analysis of NSL-KDD Dataset Using Classification Algorithms with Different Feature Selection Algorithms and Supervised Filter Discretization

  • Shailesh Singh PanwarEmail author
  • Y. P. Raiwani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 989)

Abstract

Naive Bayes and Bayes Net are critical classification methods in data mining classification and have build up being important software tools for the classification, the description and generalization of information. All classification algorithms are open sources, which are implemented on Java (C4.5 algorithms) for WEKA software tool. In this paper, we exhibit the strategy for increasing the performance of Naive Bayes and Bayes Net algorithms with supervised filter discretization after we applied feature selection techniques. We have used the supervised filter discretization on two classification algorithms and compared the result with and without discretization. The outcomes acquired from the experiment showed significant improvement over the existing classification algorithms.

Keywords

Naive Bayes Bayes net WEKA NSL-KDD dataset Preprocessing Discretization Feature selection algorithms 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.H.N.B. Garhwal UniversitySrinagar, GarhwalIndia

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