|Modeling Vague Data with Genetic Fuzzy Systems under a Combination of Crisp and Imprecise Criteria
|Year of Publication
|Sánchez, L., Couso I., and Casillas J.
|2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making
|Additive noise, combination, Computer science, crisp objectives, defuzzified training data, fuzzy logic, fuzzy models, fuzzy objectives, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, Genetic Fuzzy Systems, Global Positioning System, mean squared error, multicriteria genetic algorithms, Noise measurement, NSGA-II algorithm, Position measurement, Probability distribution, Stochastic resonance, Training data, vague data modeling
Multicriteria genetic algorithms can produce fuzzy models with a good balance between their precision and their complexity. The accuracy of a model is usually measured by the mean squared error of its residual. When vague training data is used, the residual becomes a fuzzy number, and it is needed to optimize a combination of crisp and fuzzy objectives in order to learn balanced models. In this paper, we will extend the NSGA-II algorithm to this last case, and test it over a practical problem of causal modeling in marketing. Different setups of this algorithm are compared, and it is shown that the algorithm proposed here is able to improve the generalization properties of those models obtained from the defuzzified training data.