Rule Extraction with Rough-Fuzzy Hybridization Method View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Nan-Chen Hsieh

ABSTRACT

This study presents a rough-fuzzy hybridization method to generate fuzzy if-then rules automatically from a medical diagnosis dataset with quantitative data values, based on fuzzy set and rough set theory. The proposed method consists of four stages: preprocessing inputs with fuzzy linguistic representation; rough set theory in finding notable reducts; candidate fuzzy if-then rules generation by data summarization, and truth evaluation the effectiveness of fuzzy if-then rules. The main contributions of the proposed method are the capability of fuzzy linguistic representation of the fuzzy if-then rules, finding concise fuzzy if-then rules from medical diagnosis dataset, and tolerance of imprecise data. More... »

PAGES

890-895

References to SciGraph publications

  • 1982-10. Rough sets in INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
  • Book

    TITLE

    Advances in Knowledge Discovery and Data Mining

    ISBN

    978-3-540-68124-3
    978-3-540-68125-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-68125-0_89

    DOI

    http://dx.doi.org/10.1007/978-3-540-68125-0_89

    DIMENSIONS

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