@article {4286977,
title = {Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems},
journal = {IEEE Transactions on Fuzzy Systems},
volume = {15},
number = {4},
year = {2007},
month = {Aug},
pages = {551-562},
abstract = {In our opinion, and in accordance with current literature, the precise contribution of genetic fuzzy systems to the corpus of the machine learning theory has not been clearly stated yet. In particular, we question the existence of a set of problems for which the use of fuzzy rules, in combination with genetic algorithms, produces more robust models, or classifiers that are inherently better than those arising from the Bayesian point of view. We will show that this set of problems actually exists, and comprises interval and fuzzy valued datasets, but it is not being exploited. Current genetic fuzzy classifiers deal with crisp classification problems, where the role of fuzzy sets is reduced to give a parametric definition of a set of discriminant functions, with a convenient linguistic interpretation. Provided that the customary use of fuzzy sets in statistics is vague data, we propose to test genetic fuzzy classifiers over imprecisely measured data and design experiments well suited to these problems. The same can be said about genetic fuzzy models: the use of a scalar fitness function assumes crisp data, where fuzzy models, a priori, do not have advantages over statistical regression.},
keywords = {Bayesian methods, data handling, Design for experiments, discriminant function, fuzzy fitness function, fuzzy rule-based classifiers, fuzzy rule-based models, fuzzy rules, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithm, genetic algorithms, genetic fuzzy classifiers, Genetic Fuzzy Systems, learning (artificial intelligence), linguistic interpretation, machine learning, machine learning theory, Parametric statistics, pattern classification, Robustness, scalar fitness function, statistical analysis, Stochastic resonance, vague data},
issn = {1063-6706},
doi = {10.1109/TFUZZ.2007.895942},
author = {L. S{\'a}nchez and I. Couso}
}
@article {843495,
title = {Interval-valued GA-P algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = {4},
number = {1},
year = {2000},
month = {April},
pages = {64-72},
abstract = {When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.},
keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
issn = {1089-778X},
doi = {10.1109/4235.843495},
author = {L. S{\'a}nchez}
}