GF-Miner: a Genetic Fuzzy Classifier for Numerical Data

  • Vicky Tsikolidaki
  • Nikos Pelekis
  • Yannis Theodoridis
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Fuzzy logic and genetic algorithms are well-established computational techniques that have been employed to deal with the problem of classification as this is presented in the context of data mining. Based on Fuzzy Miner which is a recently proposed state-of-the-art fuzzy rule based system for numerical data, in this paper we propose GF-Miner which is a genetic fuzzy classifier that improves Fuzzy Miner firstly by adopting a clustering method for succeeding a more natural fuzzy partitioning of the input space, and secondly by optimizing the resulting fuzzy if-then rules with the use of genetic algorithms. More specifically, the membership functions of the fuzzy partitioning are extracted in an unsupervised way by using the fuzzy c- means clustering algorithm, while the extracted rules are optimized in terms of the volume of the rulebase and the size of each rule, using two appropriately designed genetic algorithms. The efficiency of our approach is demonstrated through an extensive experimental evaluation using the IRIS benchmark dataset.


Genetic Algorithm Membership Function Fuzzy Rule Fuzzy Partitioning Fuzzy Rule Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Vicky Tsikolidaki
    • 1
  • Nikos Pelekis
    • 1
  • Yannis Theodoridis
    • 1
  1. 1.Dept of InformaticsUniv of PiraeusPiraeusGreece

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