Improving ID3 algorithm by ignoring minor instances

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dc.contributor.authorKaewrod, Nicha
dc.contributor.authorJearanaitanakij, Kietikul
dc.date.accessioned2026-08-06T10:20:26Z
dc.date.available2026-08-06T10:20:26Z
dc.date.issued2018-07-02
dc.description.abstractAmong various classification algorithms, ID3 is one of the most widely used and well-known tools that generates an efficient decision tree. Nevertheless, ID3 is too rigorous in generating the decision rules. As a result, the final decision tree may carry too many decision rules. Some of these decision rules may have very low number of instances which do not make significant change to the classification accuracy. The aim of this paper is to propose an approach to relax the rigorousness of the conventional ID3 algorithm by ignoring minor instances so that the resulting decision tree will have the lower number of depths yet produce promising accuracy. The proposed algorithm is examined on six datasets from UCI repository and Weka. The experimental results indicate that the proposed algorithm not only significantly reduces the maximum number of depths of the decision tree, but also retains the classification accuracy in the satisfying level. Moreover, the training time, the classification time, and the number of decision rules of the proposed algorithm are lower than those of the conventional ID3.
dc.identifier.citation2018 22nd International Computer Science and Engineering Conference Icsec 2018, 2018
dc.identifier.doi10.1109/ICSEC.2018.8712762
dc.identifier.other2-s2.0-85066494885
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8705
dc.source2018 22nd International Computer Science and Engineering Conference Icsec 2018
dc.subjectClassification
dc.subjectDecision rule
dc.subjectDecision tree
dc.subjectID3 algorithm
dc.titleImproving ID3 algorithm by ignoring minor instances
dc.typeConference Paper

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