Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems

TitleAdvocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems
Publication TypeJournal Article
Year of Publication2007
AuthorsSánchez, L., and Couso I.
JournalIEEE Transactions on Fuzzy Systems
Date PublishedAug
KeywordsBayesian 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

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.