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An Attribute-Centre Based Decision Tree Classification Algorithm
Authors: Gökhan Silahtaroğlu
Abstract:Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084171Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1058
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