Genetic learning of fuzzy integrals accumulating human-reported environmental stress

Authors:
A. Verkeyn;D. Botteldooren;B. De Baets
Affiliations:
Department of Information Technology, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department of Information Technology, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, B-9000 Gent, Belgium
Venue:
Applied Soft Computing
Year:
2011

Citing 11
Cited 1

Fuzzy integral in multicriteria decision making

Fuzzy Sets and Systems - Special issue on fuzzy information processing
Generalized fuzzy integrals of set-valued functions

Fuzzy Sets and Systems
A rational consensus model in group decision making using linguistic assessments

Fuzzy Sets and Systems
Fuzzy-valued fuzzy measures and generalized fuzzy integrals

Fuzzy Sets and Systems
Generalizations of k-order additive discrete fuzzy measures

Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
Alternative representations of discrete fuzzy measures for decision making

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Special issue on fuzzy measures and integrals in subjective evaluation
Optimization issues for fuzzy measures

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - A special issue on fuzzy measures
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence

Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference

Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Loss optimal monotone relabeling of noisy multi-criteria data sets

Information Sciences: an International Journal
Sugeno integrals for the modelling of noise annoyance aggregation

IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems

On the random generation of monotone data sets

Information Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.