A rough set approach for the discovery of classification rules in interval-valued information systems

  • Authors:

  • Yee Leung;Manfred M. Fischer;Wei-Zhi Wu;Ju-Sheng Mi

  • Affiliations:

  • Department of Geography and Resource Management, Center for Environmental Policy and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, ...;Institute for Economic Geography and GIScience, Vienna University of Economics and Business Administration, Nordbergstr, 15/4/A, A-1090 Vienna, Austria;Information College, Zhejiang Ocean University, Zhoushan, Zhejiang 316004, PR China;College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei 050016, PR China

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2008

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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.