The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

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 …

Since minority samples are substantially less common than majority samples, many
industrial applications, such as credit card fraud detection (CCFD) and defective part …

Recent years have witnessed the proliferation of techniques for streaming data mining to
meet the demands of many real-time systems, where high-dimensional streaming data are …

In stream learning, data continuously arrives over time, often at a very high rate. For
imbalanced data streams with concept drift, it becomes essential to simultaneously address …

Great efforts have been devoted to the intelligent control of autonomous systems. Yet, most
of existing methods fail to effectively handle the uncertainty of their environment and models …

Tiny machine learning (TML) is a new research area whose goal is to design machine and
deep learning (DL) techniques able to operate in embedded systems and the Internet-of …

Chunk-level real-time safety assessment of dynamic systems is a critical component of
industrial processes, which is essential to prevent hazards and reduce the risk of injury or …

In computer vision, when labeled images of the target domain are highly insufficient, it is
challenging to build an accurate classifier. Domain adaptation stands for an effective …