The Fuzzy Lattice Reasoning (FLR) Classifier for Mining Environmental Data

  • Ioannis N. Athanasiadis
Part of the Studies in Computational Intelligence book series (SCI, volume 67)

Summary. This chapter introduces a rule-based perspective on the framework of fuzzy lattices, and the Fuzzy Lattice Reasoning (FLR) classiffier. The notion of fuzzy lattice rules is introduced, and a training algorithm for inducing a fuzzy lattice rule engine from data is specified. The role of positive valuation functions for specifying fuzzy lattices is underlined and non-linear (sigmoid) positive valuation functions are proposed, that is an additional novelty of the chapter. The capacities for learning of the FLR using both linear and sigmoid functions are demonstrated in a real-world application domain, that of air quality assessment. To tackle common problems related to ambient air quality, a machine learning approach is demonstrated in two applications. The first one is for the prediction of the daily vegetation index, using a dataset from Athens, Greece. The second concerns with the estimation of quartely ozone concentration levels, using a dataset from Valencia, Spain.


Complete Lattice Valuation Function Inclusion Measure Training Pair Positive Valuation 
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

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ioannis N. Athanasiadis
    • 1
  1. 1.Sull'Intelligenza ArtificialeIstituto Dalle Molle di StudiLuganoSwitzerland

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