An extension of the attribute space of a dataset typically increases the prediction accuracy of a decision tree built for this dataset. Often attribute space is extended by randomly combining two or more attributes. In this paper, we propose a novel approach for the space extension where we only choose the combined attributes that have high classification capacity. We expect the inclu-sion of these attributes in the attribute space increases the prediction capacity of the trees built from the datasets with the extended space. We conduct experi-ments on five datasets coming from the UCI machine learning repository. Our experimental results indicate that the proposed space extension leads to the tree of higher accuracy than the case where original attribute space is used. Moreover, the experimental results demonstrate a clear superiority of the proposed tech-nique over an existing space extension technique.
Original languageEnglish
Title of host publicationICMLC 2014
EditorsXizhao Wang, Witold Pedrycz, Patrick Chan, Qiang He
Place of PublicationBerlin, Germany
Number of pages12
ISBN (Electronic)9783662456521
ISBN (Print)9783662456521
Publication statusPublished - 2014
EventInternational Conference on Machine Learning and Cybernetics - Langzhou, China, China
Duration: 13 Jul 201416 Jul 2014


ConferenceInternational Conference on Machine Learning and Cybernetics


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