Building Minimal Classification Rules for Breast Cancer Diagnosis

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dc.contributor.authorDouangnoulack, Phonethep
dc.contributor.authorBoonjing, Veera
dc.date.accessioned2026-08-06T10:20:47Z
dc.date.available2026-08-06T10:20:47Z
dc.date.issued2018-08-06
dc.description.abstractA rule based classifier is widely applied in breast cancer diagnosis. The classifier with a good performance of disease classification have been developed and highly required over the past decades. Since classification rules are derived from previous diagnosis with a large amount of features, it challenges to build a minimal number of rules with high performance while retaining all diagnosis information. The Principal Component Analysis (PCA) is known as a lossless data reduction technique with good classification performance. Therefore, this paper aims at finding the best performance classifier giving minimal classification rules by employing PCA. Based on experiment result on Wisconsin Breast Cancer data set, the J48 decision tree classifier is found to be the best among the three classifiers: J48 decision tree, Reduced Error Pruning Tree, and Random Tree.
dc.identifier.citation2018 10th International Conference on Knowledge and Smart Technology Cybernetics in the Next Decades Kst 2018, 278-281, 2018
dc.identifier.doi10.1109/KST.2018.8426198
dc.identifier.other2-s2.0-85052301034
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8818
dc.source2018 10th International Conference on Knowledge and Smart Technology Cybernetics in the Next Decades Kst 2018
dc.subjectBreast Cancer Diagnosis
dc.subjectDecision Tree
dc.subjectPCA
dc.subjectRule Based Classifer
dc.titleBuilding Minimal Classification Rules for Breast Cancer Diagnosis
dc.typeConference Paper

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