Feature selection has been an important research area in data mining, which chooses a
subset of relevant features for use in the model building. This paper aims to provide an …

Evolving systems unfolds from the interaction and cooperation between systems with
adaptive structures, and recursive methods of machine learning. They construct models and …

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …

Abstract The Denoising Autoencoder (DAE) enhances the flexibility of data stream method in
exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic …

As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …

This work investigates the evolution of traditional set theory to address complex and
ambiguous real-world phenomena. It introduces hierarchical hyperstructures and …

The feasibility of deep neural networks (DNNs) to address data stream problems still
requires intensive study because of the static and offline nature of conventional deep …

Autonomous construction of deep neural network (DNNs) is desired for data streams
because it potentially offers two advantages: proper model's capacity and quick reaction to …

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 …