Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

  • Authors:

  • Zheng Pei;Germano Resconi;Ariën J. Van Der Wal;Keyun Qin;Yang Xu

  • Affiliations:

  • School of Computers and Mathematical-Physical Science, Xihua University, Chengdu, Sichuan 610039, China;Catholic University, Mathematics Department, Via Trieste 17, Brescia, Italy;Qubitech Scientific B.V., Eduard van Beinumstraat 17, NL-7558 DR Hengelo, The Netherlands;Department of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan 610031, China;Department of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan 610031, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2006

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Abstract

Information systems, which contain only crisp data, precise and unique attribute values for all objects, have been widely investigated. Due to the fact that in realworld applications imprecise data are abundant, uncertainty is inherent in real information systems. In this paper, information systems are called fuzzy information systems, and formalized by (objects; attributes; f), in which f is a fuzzy set and expresses some uncertainty between an object and its attribute values. To interpret and extract fuzzy decision rules from fuzzy information systems, the meta-theory based on modal logic proposed by Resconi et al. is modified. The modified meta-theory not only expresses uncertainty between objects and their attributes, but also uncertainty in the process of recognizing fuzzy information systems. In addition, according to perception computing (proposed by Zadeh), granules of fuzzy information systems can be represented by fuzzy decision rules, so that, fuzzy inference methods can be used to obtain the decision attribute of a new object. Finally, a novel way of combining evidences based on the modified meta-theory is introduced, which extends the concept of combining evidences based on Dempster-Shafer theory.