In recent years, many researchers have focused on applying the fuzzy set theory to generate fuzzy rules from training instances to deal with the Iris data classification problem. In this paper, we propose a new method to automatically generate weighted fuzzy rules from training instances by using genetic algorithms to handle the Iris data classification problem, where the attributes appearing i...

Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or “Logitboost". The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to l...

Land covers mix and high input dimension are two important issues that affect the classification accuracy of remote sensing images. Fuzzy classification has been developed to represent the mixture of land covers. Two fuzzy classifiers of fuzzy rules-based (FRB) and fuzzy neural network (FNN) were studied to illustrate the interpretability of fuzzy classification. Based on the FNN classifier, a ...

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some...

The purpose of this paper is to study the problem of pattern classification as this is presented in the context of data mining. Among the various approaches we focus on the use of Fuzzy Logic for pattern classification, due to its close relation to human thinking. More specifically, this paper presents a heuristic fuzzy method for the classification of numerical data, followed by the design and...