Many real world problems need to deal with uncertainty, therefore the management of such
uncertainty is usually a big challenge. Hence, different proposals to tackle and manage the …

Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to
deal with the modelling of systems by building a linguistic model which could become …

The aim of any data mining technique is to build an efficient predictive or descriptive model
of a large amount of data. Applications of evolutionary algorithms have been found to be …

Over the past few decades, fuzzy systems have been widely used in several application
fields, thanks to their ability to model complex systems. The design of fuzzy systems has …

Fuzzy systems are universally acknowledged as valuable tools to model complex
phenomena while preserving a readable form of knowledge representation. The resort to …

Classification techniques are becoming essential in the financial world for reducing risks
and possible disasters. Managers are interested in not only high accuracy, but in …

Obtaining good classification results using supervised learning methods is critical if we want
to obtain a high level of precision in the classification processes. The training data used for …

This paper proposes a novel approach for building transparent knowledge-based systems
by generating accurate and interpretable fuzzy rules. The learning mechanism reported here …

A two-stage hybrid model for data classification and rule extraction is proposed. The first
stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for …

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracy-
interpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning …