Improving ID3 Algorithm by Using A∗ Search

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dc.contributor.authorKaewrod, Nicha
dc.contributor.authorJearanaitanakij, Kietikul
dc.date.accessioned2026-08-06T10:21:21Z
dc.date.available2026-08-06T10:21:21Z
dc.date.issued2018-08-21
dc.description.abstractID3 is one of the most widely used algorithms for creating a classification decision tree. However, the traditional ID3 algorithm has a difficulty when there are equally important attributes during the decision node construction. It randomly selects one of the most important attributes to serve as the current decision node. This behavior may lead to the resulting decision tree which contains unnecessary depths and suboptimal accuracy. The purpose of this paper is to find the near optimal depth decision tree of the dataset, which contains an equally important attributes problem, by using the A-Star (A∗) search. The experiment results on four standard datasets from UCI indicate that the proposed algorithm can significantly reduce the decision tree's depth and still maintain the classification accuracy.
dc.identifier.citationIcsec 2017 21st International Computer Science and Engineering Conference 2017 Proceeding, 132-135, 2018
dc.identifier.doi10.1109/ICSEC.2017.8443800
dc.identifier.other2-s2.0-85053476907
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8977
dc.sourceIcsec 2017 21st International Computer Science and Engineering Conference 2017 Proceeding
dc.subjectA
dc.subjectClassification
dc.subjectDecision tree
dc.subjectID3 algorithm
dc.subjectsearch
dc.titleImproving ID3 Algorithm by Using A∗ Search
dc.typeConference Paper

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