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dc.contributor.authorChen, Wei-Chouen_US
dc.contributor.authorYang, Ming-Chunen_US
dc.contributor.authorTseng, Shian-Shyongen_US
dc.date.accessioned2014-12-08T15:35:59Z-
dc.date.available2014-12-08T15:35:59Z-
dc.date.issued2005en_US
dc.identifier.issn1088-467Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/24338-
dc.description.abstractFeature selection is about finding useful (relevant) features to describe an application domain. The problem of finding the minimal subsets of features that can describe all of the concepts in the given data set is NP-hard. In the past, we had proposed a feature selection method, which originated from rough set and bitmap indexing techniques, to select the optimal (minimal) feature set for the given data set efficiently. Although our method is sufficient to guarantee a solution's optimality, the computation cost is very high when the number of features is huge. In this paper, we propose a nearly optimal feature selection method, called bitmap-based feature selection method with discernibility matrix, which employs a discernibility matrix to record the important features during the construction of the cleansing tree to reduce the processing time. And the corresponding indexing and selecting algorithms for such feature selection method are also proposed. Finally, some experiments and comparisons are given and the result shows the efficiency and accuracy of our proposed method.en_US
dc.language.isoen_USen_US
dc.subjectmachine learningen_US
dc.subjectknowledge discoveryen_US
dc.subjectfeature selectionen_US
dc.subjectbitmap indexingen_US
dc.subjectrough seten_US
dc.titleA novel feature selection method for large-scale data setsen_US
dc.typeArticleen_US
dc.identifier.journalINTELLIGENT DATA ANALYSISen_US
dc.citation.volume9en_US
dc.citation.issue3en_US
dc.citation.spage237en_US
dc.citation.epage251en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000202969300002-
dc.citation.woscount1-
Appears in Collections:Articles