Fuzzy classification using pattern discovery

Andrew Hamilton-Wright, Daniel W. Stashuk, Hamid R. Tizhoosh

Research output: Contribution to journalArticlepeer-review


Rule-based classifiers allow rationalization of classifications made. This in turn improves understanding which is essential for effective decision support. As a rule based classifier, the Pattern Discovery (PD) algorithm functions well in discrete, nominal and continuous data domains. A drawback when using PD as a classifier for decision support is that it has an unbounded decision space that confounds the understanding of the degree of support for a decision. Incorporating PD into a fuzzy inference system (FIS) allows the the degree of support for a decision to be expressed with intuitively understandable terms. In addition, using discrete algorithms in continuous domains can result in reduced accuracy due to quantization. Fuzzification reduces this "cost of quantization"and improves classification performance. In this work, the PD algorithm was used as a source of rules for a series of FISs implemented using different rule weighting and defuzzification schemes, each providing a linguistic basis for rule description and a [1....1] bounded space for expression of decision support. The output of each FIS consists of a suggested outcome, a strong confidence metric describing suggestions within this space and a linguistic expression of the rules. This constitutes a stronger basis for decision making than that provided by PD alone. A variety of synthetic, continuous class distributions with varying degrees of separation was used to evaluate the performance of fuzzy, PD, back-propagation and Bayesian classifiers. Overall, the accuracy of the fuzzy system was found to be similar, but slightly below, that of the inherently continuous valued classifiers and was somewhat improved with respect to the PD classifiers. For the difficult spiral class distributions studied, the fuzzy classifiers were able to make more classifications than the PD classifiers. The correct classification rates for the fuzzy classifiers were similar across the various rule weighting and defuzzification schemes, demonstrating the strength of the statistical method for rule generation. Analysis of several real-world data sets shows that a PD-based FIS has comparable performance to a neuro-fuzzy system. The use of a PD based FIS however, provides insight into the structure of the data analyzed not available through the other approaches.

Original languageEnglish (US)
Pages (from-to)772-783
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Issue number5
StatePublished - Oct 2007


  • Decision support systems
  • Deductive databases
  • Fuzzy logic
  • Fuzzy systems
  • Pattern classification
  • Pattern clustering methods
  • Pattern matching
  • Statistical databases

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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