Learning and Decision-Making in the Framework of Fuzzy Lattices

  • V. G. Kaburlasos
  • V. Petridis
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 84)


A novel theoretical framework is delineated for supervised and unsupervised learning. It is called framework of fuzzy lattices, or FL-framework for short, and it suggests mathematically sound tools for dealing separately and/or jointly with disparate types of data including vectors of numbers, fuzzy sets, symbols, etc. Specific schemes are proposed for clustering and classification having the capacity to deal with both missing and don’t care data values; the schemes in question can be implemented as neural networks. The proposed learning schemes are employed here for pattern recognition on seven data sets including benchmark data sets, and the results are compared with those ones by various learning techniques from the literature. Finally, aiming at a mutual cross-fertilization, the FL-framework is associated with established theories for learning and/or decision-making including probability theory, fuzzy set theory, Bayesian decision-making, theory of evidence, and adaptive resonance theory.


Fuzzy Number Complete Lattice Adaptive Resonance Theory Inclusion Measure Lattice Interval 
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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© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • V. G. Kaburlasos
    • 1
  • V. Petridis
    • 1
  1. 1.Department of Electrical and Computer EngineeringAristotle University of ThessalonikiThessalonikiGreece

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