Stock market prediction is regarded as a challenging task in financial time-series
forecasting. The central idea to successful stock market prediction is achieving best results …

Abstract The term Soft Computing was coined by LA Zadeh in the early 90's. Since that time
many researchers have tried to define it considering different approaches: main constituents …

In this paper, we propose an index that helps preserve the semantic interpretability of
linguistic fuzzy models while a tuning of the membership functions (MFs) is performed. The …

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup
discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is …

In this paper we propose GP-COACH, a Genetic Programming-based method for the
learning of COmpact and ACcurate fuzzy rule-based classification systems for High …

When performing a classification task, we may find some data-sets with a different class
distribution among their patterns. This problem is known as classification with imbalanced …

In this paper, we deal with a novel data-driven learning method [sparse fuzzy inference
systems (SparseFIS)] for Takagi-Sugeno (TS) fuzzy systems, extended by including rule …

Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of
the existence of several approaches, much effort is still required to increase the efficiency of …

We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front
approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs …

For diagnosing dyslexia in early childhood, children have to solve non-writing based
graphical tests. Human experts score these tests, and decide whether the children require …