Feature Selection Method Based on Correlation Tree

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dc.contributor.authorYapila, Prajak
dc.contributor.authorThreepak, Thanunchai
dc.date.accessioned2026-08-06T10:27:30Z
dc.date.available2026-08-06T10:27:30Z
dc.date.issued2020-01-01
dc.description.abstractMachine 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).
dc.identifier.citationAdvances in Intelligent Systems and Computing, 1149 AISC, 70-78, 2020
dc.identifier.doi10.1007/978-3-030-44044-2_8
dc.identifier.issn21945357
dc.identifier.other2-s2.0-85083667710
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10649
dc.sourceAdvances in Intelligent Systems and Computing
dc.subjectCyber security
dc.subjectFeature selection
dc.subjectIDS
dc.subjectKDDCup’99
dc.subjectMachine learning
dc.titleFeature Selection Method Based on Correlation Tree
dc.typeConference Paper

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