Improving ID3 Algorithm by Combining Values from Equally Important Attributes

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dc.contributor.authorKraidech, Suratchanan
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 a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional ID3 algorithm with various strategies, no attempt was made on ID3 to address the problem when there are more than one attribute that can be placed at a particular node, i.e. those attributes are equally important. This paper proposes a new variation of ID3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by ID3 algorithm while the average accuracy rate is still preserved.
dc.identifier.citationIcsec 2017 21st International Computer Science and Engineering Conference 2017 Proceeding, 102-105, 2018
dc.identifier.doi10.1109/ICSEC.2017.8443862
dc.identifier.other2-s2.0-85053472936
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8978
dc.sourceIcsec 2017 21st International Computer Science and Engineering Conference 2017 Proceeding
dc.subjectAttribute combining
dc.subjectClassification
dc.subjectDecision tree
dc.subjectID3 algorithm
dc.titleImproving ID3 Algorithm by Combining Values from Equally Important Attributes
dc.typeConference Paper

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