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 need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems.
The obtaining of accurate but also human-comprehensible fuzzy systems played a key role …

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 …

Lithology identification methods based on conventional logging data are essential in
reservoir geological evaluation. Due to the highly non-linear relationship between lithology …

As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …

Classifiers are often opaque and cannot easily be inspected to gain understanding of which
factors are of importance. We propose an efficient iterative algorithm to find the attributes …

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 …

When individual classifiers are combined appropriately, a statistically significant increase in
classification accuracy is usually obtained. Multiple classifier systems are the result of …

Feature selection (FS) is an important task which can significantly affect the performance of
image classification and recognition. In this paper, we present a feature selection algorithm …