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 …

The incompleteness of data samples in data streams always affects the performance of
learning model. In order to learn data streams with missing values from features and a large …

It is widely recognized that learning systems have to go deeper to exchange for more
powerful representational learning capabilities in order to precisely approximate nonlinear …

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 …

Currently, the increase in devices capable of continuously collecting data on non-stationary
and dynamic variables affects predictive models, particularly if they are not equipped with …

Fuzzy systems offer a formal and practically popular methodology for modeling nonlinear
problems with inherent uncertainties, entailing strong performance and model …

In this paper, a novel self-learning design of an interval type-2 hierarchical fuzzy system (IT2
HFS) based on rule relevance is proposed. Different from the existing methods, this paper …

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data
prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed …

In recent years, with the gradual development of sports, the competition between athletes is
becoming more and more fierce. The long training time and heavy body load of athletes lead …