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Genetic learning of fuzzy integrals accumulating human-reported environmental stress
Applied Soft Computing, (2011)
Аннотация
In this paper, we develop models based on fuzzy integrals (both of
the Choquet and Sugeno type) for accumulating annoyance by noise,
odor or light caused by particular sources or activities. As underlying
fuzzy measures, we have opted for k-maxitive measures (in particular
1-maxitive or 2-maxitive) as the best known crisp model points in
this direction. The fuzzy measures are learnt from survey data and
optimized using genetic algorithms. Attention is paid to several
types of inconsistencies that typically arise in data sets collected
through social surveys. Also, special care is taken to make sure
that the Sugeno integral and the genetic algorithm that optimizes
the associated fuzzy measure operates solely on the ordinal scale
of linguistic labels.
