The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rulebased systems by learning andror tuning the fuzzy rule b...

The data-driven identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A binary decision-tree-based initialization of fuzzy classifiers is proposed for the selection of the relevant features and effective initial partitioning of the input domains of the fuzzy system. Fuzzy classifiers have more flexible decision boundaries than decision trees (DTs) and can th...

In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. The proposed FNN can handle numeric and fuzzy inputs simultaneously. The numeric inputs are fuzzified by input nodes upon presentation to the network while the Fuzzy rule based knowledge is translated directly into network architecture. The connections between input to hidden nodes represent rule ...

In the present study, we propose a novel clustering-based method for modeling accurate fuzzy rule-based classification systems. The new method is a combination of a data mapping method, subtractive clustering method and an efficient gradient descent algorithm. A data mapping method considers the intricate geometric relationships that may exist among the data and computes a new representation of...

In this work, we conduct a preliminary study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging combined with feature selection. We develop a study on the use of both bagging and...

In the recent years, there has been growing interest in developing tools for the modeling and prediction of financial applications. The problem of financial applications is that there are huge data sets available which are sometimes incomplete, and almost always affected by noise and uncertainty. Some techniques used in financial applications employ black box models which do not allow the user ...

The architecture and implementation of a rule-based fuzzy approach to the detection and classification of man made objects in satellite image data are presented. In a first processing step fuzzy clustering is used to obtain an initial coarse presegmentation of the data. Subsequently, a rule-based fuzzy system performs the classification. Its rules are generated both manually (based on common-se...

Fuzzy sets as proposed by Zadeh (1965) are a generalization of classical set theory, in which objects, instead of just being members of a set or not, have a gradual degree of membership. Fuzzy rule-based systems (FRBS) have been used in the past successfully in many applications. They are competitive methods for classification and regression, especially for complex problems. One of their leadin...

Nowadays, one of the most important areas of application of fuzzy set theory are fuzzy rule-based systems. These kinds of systems constitute an extension of classical rule-based systems, because they deal with “IF-THEN” rules whose antecedents and consequents are composed of fuzzy logic statements instead of classical logic. They have been successfully applied to a wide range of problems in dif...

Genetic Algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules from data for systems modeling and classification have received much attention in recent literature. We show that different tools for modeling and complexity reduction can be favorably combined in a scheme with GA-based parameter optimization. Fuzzy clustering, rule reduction, rule base simplification and...