A novel discretizer for knowledge discovery approaches based on rough sets

被引:0
作者
Wu, Qingxiang [1 ]
Cai, Jianyong
Prasad, Girijesh
McGinnity, T. M.
Bell, David
Guan, Jiwen
机构
[1] Fujian Normal Univ, Sch Phys & OptoElect Technol, Fujian 350007, Fuzhou, Peoples R China
[2] Univ Ulster, Sch Comp & Intelligent Syst, Londonderry BT48 7JL, North Ireland
[3] Queens Univ Belfast, Sch Comp Sci, Belfast, Antrim, North Ireland
来源
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS | 2006年 / 4062卷
关键词
knowledge discovery; rough sets; continuous attribute discretization; decision-making; data preparation;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed to preprocess continuous valued attributes in an instance information system, so that the knowledge discovery approaches based on rough sets can reach a high decision accuracy. The experimental results on benchmark data sets show that the proposed discretizer is able to improve the decision accuracy.
引用
收藏
页码:241 / 246
页数:6
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