M. J. del Jesus

Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely developed task. It involves adjusting some of the components of the knowledge base without completely redefining it. This contribution introduces a genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the involved membership functions. To adjust the former component, we propose the use of linguistic hedges to perform slight modifications keeping a good interpretability. To alter the latter component, two different approaches changing their basic parameters and using nonlinear scaling factors are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two different levels of significance. The paper also analyzes the interaction of the proposed tuning method with a fuzzy rule set reduction process. A good interpretability-accuracy tradeoff is obtained combining both processes with a sequential scheme: first reducing the rule set and subsequently tuning the model.

Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems

Abstract The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods.

Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms

This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algorithms address the cooperation-competition problem. The results are used to explain some properties of the former method. The evolutionary boosting scheme is applied to approximate and descriptive fuzzy-rule bases. The advantages of boosting fuzzy rules are assessed by performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks.

MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule Learning approach

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 rule‐based systems by learning and/or tuning the fuzzy rule base, following the same generic structure and able to cope with problems of a different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdani‐type fuzzy rule‐based systems will be introduced, and its accuracy in the solving of a real‐world electrical engineering problem will be analyzed. ©1999 John Wiley & Sons, Inc.

GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.

NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is based on the hybridization between fuzzy logic and genetic algorithms, deals with subgroup-discovery problems in order to extract novel and interpretable fuzzy rules of interest, and the evolutionary fuzzy system NMEEF-SD is based on the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II) model but is oriented toward the subgroup-discovery task using specific operators to promote the extraction of interpretable and high-quality subgroup-discovery rules. The proposal includes different mechanisms to improve diversity in the population and permits the use of different combinations of quality measures in the evolutionary process. An elaborate experimental study, which was reinforced by the use of nonparametric tests, was performed to verify the validity of the proposal, and the proposal was compared with other subgroup discovery methods. The results show that NMEEF-SD obtains the best results among several algorithms studied.

Web usage mining to improve the design of an e-commerce website: OrOliveSur.com

Web usage mining is the process of extracting useful information from users history databases associated to an e-commerce website. The extraction is usually performed by data mining techniques applied on server log data or data obtained from specific tools such as Google Analytics. This paper presents the methodology used in an e-commerce website of extra virgin olive oil sale called www.OrOliveSur.com. We will describe the set of phases carried out including data collection, data preprocessing, extraction and analysis of knowledge. The knowledge is extracted using unsupervised and supervised data mining algorithms through descriptive tasks such as clustering, association and subgroup discovery; applying classical and recent approaches. The results obtained will be discussed especially for the interests of the designer team of the website, providing some guidelines for improving its usability and user satisfaction.

A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems

We propose a new method to automatically learn the knowledge base of a fuzzy rule-based classification system (FRBCS) by selecting an adequate set of features and by finding an appropiate granularity for them. This process uses a multiobjective genetic algorithm and considers a simple generation method to derive the fuzzy classification rules.

Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study on the type of patients who tend to visit the psychiatric emergency department in a given period of time of the day. In this problem, the objective is to characterise subgroups of patients according to their time of arrival at the emergency department. To solve this problem, several subgroup discovery algorithms have been applied to determine which of them obtains better results. The multiobjective evolutionary algorithm MESDIF for the extraction of fuzzy rules obtains better results and so it has been used to extract interesting information regarding the rate of admission to the psychiatric emergency department.

A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks

This paper presents a new multiobjective cooperative–coevolutive hybrid algorithm for the design of a Radial Basis Function Network (RBFN). This approach codifies a population of Radial Basis Functions (RBFs) (hidden neurons), which evolve by means of cooperation and competition to obtain a compact and accurate RBFN. To evaluate the significance of a given RBF in the whole network, three factors have been proposed: the basis function’s contribution to the network’s output, the error produced in the basis function radius, and the overlapping among RBFs. To achieve an RBFN composed of RBFs with proper values for these quality factors our algorithm follows a multiobjective approach in the selection process. In the design process, a Fuzzy Rule Based System (FRBS) is used to determine the possibility of applying operators to a certain RBF. As the time required by our evolutionary algorithm to converge is relatively small, it is possible to get a further improvement of the solution found by using a local minimization algorithm (for example, the Levenberg–Marquardt method). In this paper the results of applying our methodology to function approximation and time series prediction problems are also presented and compared with other alternatives proposed in the bibliography.