The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Most machine learning models are static, but the world is dynamic, and increasing online
deployment of learned models gives increasing urgency to the development of efficient and …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

As an emerging research topic, online class imbalance learning often combines the
challenges of both class imbalance and concept drift. It deals with data streams having very …

Billions of dollars of loss are caused every year due to fraudulent credit card transactions.
The design of efficient fraud detection algorithms is key for reducing these losses, and more …

Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

Every day, huge volumes of sensory, transactional, and web data are continuously
generated as streams, which need to be analyzed online as they arrive. Streaming data can …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

Class imbalance is an important problem, encountered in machine learning applications,
where one class (named as, the minority class) has extremely small number of instances …