Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

Multi-class imbalanced classification is more difficult and less frequently studied than its
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …

Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical
machine learning algorithms offer high prediction performance, but most of them lack …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

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 …

This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …

Due to the complexity of their working conditions, historical rolling bearing datasets are
mostly limited and imbalanced. The fault data may be composed of multiple subclusters; that …

Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current
guidelines are not well suited for diagnosing patients in the early stages of disease and do …

The data imbalance problem in classification is a frequent but challenging task. In real-world
datasets, numerous class distributions are imbalanced and the classification result under …

Learning from imbalanced data sets is known to be a challenging task. There are many
proposals to tackle the challenge for classification problems, but regarding regression the …