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 …

Abstract Socially Assistive Robots (SARs) are increasingly used in dementia and elderly
care. In order to provide effective assistance, SARs need to be personalized to individual …

Existing research mainly uses prior knowledge to set all fuzzy sets in a fuzzy system to the
same type. In data-driven fuzzy modeling, automatically determining the type of fuzzy set is …

Time series are a kind of streaming data, which are chaotic and sequential. As real-world
time series data are often not available at once and drift with time growth, Incremental …

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 …

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 …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …