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

Author

Listed:
  • Leung, Yee
  • Fischer, Manfred M.
  • Wu, Wei-Zhi
  • Mi, Ju-Sheng

Abstract

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient 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 optimal 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 & Fischer, Manfred M. & Wu, Wei-Zhi & Mi, Ju-Sheng, 2008. "A rough set approach for the discovery of classification rules in interval-valued information systems," MPRA Paper 77767, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:77767
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    File URL: https://mpra.ub.uni-muenchen.de/77767/1/MPRA_paper_77767.pdf
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    References listed on IDEAS

    as
    1. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    2. Leung, Yee & Wu, Wei-Zhi & Zhang, Wen-Xiu, 2006. "Knowledge acquisition in incomplete information systems: A rough set approach," European Journal of Operational Research, Elsevier, vol. 168(1), pages 164-180, January.
    3. P. J. Lingras & Y. Y. Yao, 1998. "Data mining using extensions of the rough set model," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(5), pages 415-422.
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    Cited by:

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    2. Yi-Shian Lee & Lee-Ing Tong, 2012. "Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming," Energies, MDPI, vol. 5(3), pages 1-16, February.

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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