This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

Handling uncertainty and vagueness in real world becomes a necessity for developing intelligent and efficient systems. Based on the credibility theory, a fuzzy clustering approach that improves the classification accuracy is targeted by this work. This paper introduces a design of an efficient set of fuzzy rules that are inferred by a hybrid model of SOFM (Self Organized Features Maps) and FRAN...

This paper shows how a small number of fuzzy rules can be selected for designing interpretable fuzzy rule-based classification systems. Our approach consists of two phases: candidate rule generation by data mining criteria and rule selection by genetic algorithms. First a large number of candidate rules are generated and prescreened using two rule evaluation criteria in data mining. Next a smal...

The inductive learning of fuzzy rule-based classification systems usually encounters an issue of exponential growth of the fuzzy rulesearch space when the number of patternsand/or variables becomes large.This issue makes the learning process more difficultand, in most cases, may lead to scalability problems. Alcalá-Fdezet al.proposed a fuzzy association rule-based classification method for high...

The proposed method develops a fuzzy rule-based classifier that was tested using features for islanding detection in distributed generation. In the developed technique, the initial classification boundaries are found out by using the decision tree (DT). From the DT classification boundaries, the fuzzy membership functions (MFs) are developed and the corresponding rule base is formulated for isl...

In the recent years, a high number of fuzzy rule learning algorithms have been developed with the aim of building the Knowledge Base of Linguistic Fuzzy Rule Based Systems. In this context, it emerges the necessity of managing a flexible structure of the Knowledge Base with the aim of modeling the problems with a higher precision. In this work, we present a short overview on the Hierarchical Fu...

Fuzzy rule-based systems have shown a high capability of knowledge extraction and representation when modeling complex, nonlinear classification problems. However, they suffer from the so-called curse of dimensionality when applied to high dimensional datasets, which consist of a large number of variables and/or examples. Multiclassification systems have shown to be a good approach to deal with...

Abstract This paper develops and compares two fuzzy logic based and a traditional rule-based pattern recognition system, which perform target recognition with data from a typical range and doppler resolving radar. The parameters used by the pattern recognition systems are target altitude, velocity, range from nearest base, and radar cross section. The pattern recognition systems identify four c...

Fuzzy classification rules are widely considered a well-suited representation of classification knowledge, as they allow readable and interpretable rule bases. The goal of this paper is to discuss the shapes of the resulting classification borders under consideration of different types of fuzzy sets, rule bases and t-norms and thus which class distributions can be represented by such classifica...