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 …

An evolving intelligent system (EIS) is able to self-update its system structure and meta-
parameters from streaming data. However, since the majority of EISs are implemented on a …

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 …

As a powerful tool for data streams processing, the vast majority of existing evolving
intelligent systems (EISs) learn prediction models from data in a supervised manner …

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 …

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 …

The identification and mitigation of nonline-of-sight (NLOS) paths play crucial roles in
effectively localizing sensor nodes deployed in both indoor and urban outdoor …

Prototype-based approaches generally provide better explainability and are widely used for
classification. However, the majority of them suffer from system obesity and lack …

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated
strong performance on many real-world problems concerning data stream classification …

Images (HSIs) are popular in diversified applications, such as geosciences, biomedical
imaging, molecular biology, agriculture, astronomy, food quality and safety assessment …