Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Abstract Machine learning (ML) is widely used to uncover structure–property relationships of
materials due to its ability to quickly find potential data patterns and make accurate …

Automated machine learning (AutoML) strives for the automatic configuration of machine
learning algorithms and their composition into an overall (software) solution—a machine …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Imbalanced learning is one of the most relevant problems in machine learning. However, it
faces two crucial challenges. First, the amount of methods proposed to deal with such …

With the development of booming AutoML systems, modeling processes have become more
automatic for researchers. However, AutoML systems may struggle to identify the optimal …

Abstract Neural Architecture Search (NAS) is well-known for automatizing neural
architecture design and finding better architectures. Although NAS methods have shown …