基於類-屬性關聯度的啟發式離散化技術

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 期刊論文 > Item 987654321/90308

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题名:	基於類-屬性關聯度的啟發式離散化技術
其它题名:	Heuristic discretization technique based on the class-attribute interdependence
作者:	周世昊;倪衍森
贡献者:	淡江大學管理科學學系
关键词:	離散化;數據挖掘;自頂向下;變精度粗糙集;不一致;discretization;data mining;top-down;variable precision rough sets;inconsistency
日期:	2011-10
上传时间:	2013-06-10 16:25:30 (UTC+8)
出版者:	瀋陽市：東北大學
摘要:	Discretization algorithms play an important role in many areas such as data mining, machine learning and artificial intelligence. Therefore, a heuristic discretization technique based on the class-attribute interdependence is proposed. A new discretization criterion is defined, which selects best cut points in terms of characteristics of the data itself and overcomes the existing deficiencies of state-of-the-art top-down discretization methods. A novel measure of inconsistency based on variable precision rough sets(VPRS) model is developed, which effectively controls information loss generated by discretization and allows an adaptive degree of misclassification. Empirical experiments and statistical analysis show that the proposed technique generates a better discretization scheme which significantly improves the accuracy of classification by running J4.8 and SVM.
關聯:	控制與決策=Control and Decision 26(10)，頁1504-1510
显示于类别:	[管理科學學系暨研究所] 期刊論文

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