基於類-屬性關聯度的啟發式離散化技術

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Title:	基於類-屬性關聯度的啟發式離散化技術
Other Titles:	Heuristic discretization technique based on the class-attribute interdependence
Authors:	周世昊;倪衍森
Contributors:	淡江大學管理科學學系
Keywords:	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
Date:	2011-10
Issue Date:	2013-06-10 16:25:30 (UTC+8)
Publisher:	瀋陽市：東北大學
Abstract:	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.
Relation:	控制與決策=Control and Decision 26(10)，頁1504-1510
Appears in Collections:	[Department of Management Sciences] Journal Article

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