This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each...

Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to mis...

Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, sec...

The degree of vagueness of variables, process description, and automation functions is considered and is shown where quantitative and qualitative knowledge is available for design and information processing within automation systems. Fuzzy-rule-based systems with several levels of rules form the basis for different automation functions. Fuzzy control can be used in many ways, for normal and for...

In many real application areas, the data used are highly skewed and the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this paper is to improve the performance of fuzzy rule based classification systems on imb...

In this paper we have introduced a non interactive model for fuzzy rule based systems A critical aspects of this non interactive model is the introduction of a new set of rules with fewer parameters and without considering the interaction between the functionality of inputs The new non interactive model of the fuzzy rule based system represents the output as a linear combination of the non line...

Imbalanced classification problems are attracting the attention of research community because they prevalent in real-world and impose extra difficulties for learning methods. Fuzzy rule-based systems have been applied to cope with these problems, mostly together sampling techniques. In this paper, we define a new fuzzy association classifier, named FARCI, tackle directly imbalanced problems. Ou...