A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR)

  • Authors:

  • Vassilis G. Kaburlasos;S. E. Papadakis

  • Affiliations:

  • Technological Educational Institution of Kavala, Department of Industrial Informatics, GR-65404 Kavala, Greece;Technological Educational Institution of Kavala, Department of Industrial Informatics, GR-65404 Kavala, Greece

  • Venue:
  • Neurocomputing
  • Year:
  • 2009

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Abstract

The fuzzy lattice reasoning (FLR) classifier was introduced lately as an advantageous enhancement of the fuzzy-ARTMAP (FAM) neural classifier in the Euclidean space R^N. This work extends FLR to space F^N, where F is the granular data domain of fuzzy interval numbers (FINs) including (fuzzy) numbers, intervals, and cumulative distribution functions. Based on a fundamentally improved mathematical notation this work proposes novel techniques for dealing, rigorously, with imprecision in practice. We demonstrate a favorable comparison of our proposed techniques with alternative techniques from the literature in an industrial prediction application involving digital images represented by histograms. Additional advantages of our techniques include a capacity to represent statistics of all orders by a FIN, an introduction of tunable (sigmoid) nonlinearities, a capacity for effective data processing without any data normalization, an induction of descriptive decision-making knowledge (rules) from the training data, and the potential for input variable selection.