A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems

32 Pages Posted: 13 Jan 2017

See all articles by Yee Leung

Yee Leung

The Chinese University of Hong Kong (CUHK)

Manfred M. Fischer

Vienna University of Economics and Business - Institute for Economic Geography and GIScience, Department of Socioeconomics

Wei-Zhi Wu

Zhejiang Ocean University

Ju-Sheng Mi

Hebei Normal University

Date Written: 2008

Abstract

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The minimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

Suggested Citation

Leung, Yee and Fischer, Manfred M. and Wu, Wei-Zhi and Mi, Ju-Sheng, A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems (2008). Available at SSRN: https://ssrn.com/abstract=2898125 or http://dx.doi.org/10.2139/ssrn.2898125

Yee Leung

The Chinese University of Hong Kong (CUHK) ( email )

Department of Geography and Center for Environmental Studies
Shatin, N.T., Hong Kong
China

Manfred M. Fischer (Contact Author)

Vienna University of Economics and Business - Institute for Economic Geography and GIScience, Department of Socioeconomics ( email )

Welthandelsplatz 1, D4
Vienna, 1020
Austria

Wei-Zhi Wu

Zhejiang Ocean University ( email )

No. 1 Dinghai District
Lincheng streets Haid Road
Zhoushan City, Zhejiang 316022
China

Ju-Sheng Mi

Hebei Normal University ( email )

No.20 Nanerhuandong Road, Shijiazhuang 050024
Shijiazhuang, Hebei 050024
China

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