Automated machine learning (AutoML) aims to automate machine learning (ML) tasks,
eliminating human intervention from the learning process as much as possible. However …

The existing malware classification approaches (ie, binary and family classification) can
barely benefit subsequent analysis with their outputs. Even the family classification …

With most technical fields, there exists a delay between fundamental academic research and
practical industrial uptake. Whilst some sciences have robust and well-established …

Current methods for classifying IoT malware predominantly utilize binary and family
classifications. However, these outcomes lack the detailed granularity to describe malicious …

Multi-label classification has been used to solve a wide range of problems where each
example in the dataset may be related either to one class (as in traditional classification …

Missing data is a prevalent problem that requires attention, as most data analysis techniques
are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where …

Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small
molecule properties essential for develo** new drugs. These properties-including …

Infants' neurological development is heavily influenced by their motor skills. Evaluating a
baby's movements is key to understanding possible risks of developmental disorders in their …

Network Intrusion Detection Systems (NIDS) observe a network environment and aim to
identify intrusions: malicious behaviors that compromise integrity, confidentiality or …

Abstract Automated Machine Learning (AutoML) has achieved high popularity in recent
years. However, most of these studies have investigated alternatives to single-label …