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 …

Smartphones enable to collect large data streams about phone calls that, once combined
with Computational Intelligence techniques, bring great potential for improving the …

E-commerce is snowballing with advancements in technology, and as a result,
understanding complex transactional data has become increasingly important. To keep …

Financial markets and weather prediction are generating streaming data at a rapid rate. The
frequent concept drifts in these data streams pose significant challenges to learners during …

Abstract Concept drift refers to the inevitable phenomenon that influences the statistical
features of the data stream. Detecting concept drift in data streams quickly and precisely …

Data streams are sequences of fast-growing and high-speed data points that typically suffer
from the infinite length, large volume, and specifically unstable data distribution. Ensemble …

High-dimensional data classification is widely considered as a challenging task in machine
learning due to the so-called “curse of dimensionality.” In this article, a novel multilayer …

Ensemble learning is a widely used methodology to build powerful predictors from multiple
individual weaker ones. However, the vast majority of ensemble learning models are …

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble
classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are …

There are a plethora of invented classifiers in Machine learning literature, however, there is
no optimal classifier in terms of accuracy and time taken to build the trained model …