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Feature Selection Method Based on Correlation Tree

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Recent Advances in Information and Communication Technology 2020 (IC2IT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1149))

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Abstract

Machine learning is one of techniques adapted to detect intrusion for cyber security. One of importance techniques to find anomaly is classification. But classification with huge dataset has the resources and time consumption. Feature selection is choice to reduce the data dimension to improve processing performance. In this paper, we introduce the new feature selection method that selects some fields of data set using position of each feature in correlation tree. Then, the result from the correlation tree feature selection of KDDCUP’99 data set are compared with two feature selection technique, correlation of coefficient (CC-type) and BFS by using three reference classifier, Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB).

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Authors and Affiliations

  1. Defence Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Prajak Yapila

  2. Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Thanunchai Threepak

Authors
  1. Prajak Yapila
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  2. Thanunchai Threepak
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Corresponding author

Correspondence to Prajak Yapila .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

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Yapila, P., Threepak, T. (2020). Feature Selection Method Based on Correlation Tree. In: Meesad, P., Sodsee, S. (eds) Recent Advances in Information and Communication Technology 2020. IC2IT 2020. Advances in Intelligent Systems and Computing, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-030-44044-2_8

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