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 diierent Genetic Fuzzy Rule-Based Systems, i. e., evolutionary algorithm-based processes to automatically design Fuzzy Rule-Based Systems by learning and/or tuning the Fuzzy Rule ...

The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their work...

In this work, we conduct a 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 and feature selection. We develop an exhaustive study on the potential of bagging and feature ...

This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic programming system for the generation of fuzzy rule-based systems. Two different medical domains are used ...

In this paper we are dealing with the construction of a fuzzy rule based classifier. A three-step method is proposed based on Lukasiewicz logic for the description of the rules and the fuzzy memberships to construct concise and highly comprehensible fuzzy rules. In our method, a genetic algorithm is applied to evolve the structure of the rules and then a gradient based optimization to fine tune...

The paper presents a generalization of the Pittsburgh approach to learn fuzzy classification rules from data. The proposed approach allows us to obtain a fuzzy rule-based system with a predefined level of compromise between its accuracy and interpretability (transparency). The application of the proposed technique to design the fuzzy rule-based classifier for the well known benchmark data sets ...

Pattern classification algorithms have been applied in data mining and signal processing to extract the knowledge from data in a wide range of applications. The Fuzzy inference systems have successfully been used to extract rules in rule-based applications. In this paper, a novel hybrid methodology using: (i) fuzzy logic (in form of if−then rules) and (ii) a bio-inspired optimization technique ...

This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm’s computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles ...

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivale...

This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule base fuzzy system (SRBFS) by linguisti...