Abstract Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems
and Evolutionary Algorithms. They integrate both the management of imprecision …

Interval-valued intuitionistic fuzzy sets are widely used in multi-attribute decision-making
problems to select the optimal alternative, but how to measure uncertainty is an open and …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

Identifying the factors that influence student academic performance is essential to provide
timely and effective support interventions. The data collected during enrolment and after …

In this investigation, we outline the applications of a data mining technique known as
Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based …

This paper proposes a novel algorithm for subgroup discovery task based on genetic
programming and fuzzy logic called Fuzzy Genetic Programming-based for Subgroup …

Nowadays, there is an incredible increase of data volumes around the world, with the
Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The …

Fuzzy classification systems have been widely researched with many approaches proposed
in the literature. Several methods are available for the automatic definition of fuzzy …

The rise of infections caused by multidrug-resistant bacteria has become a very important
issue for health institutions around the world, urging them to a more appropriate use of …

Emerging pattern mining is a data mining task that belongs to the supervized descriptive rule
discovery framework. Its objective is to find rules that describe emerging behavior or …