Deep metric learning (DML) has been widely applied in various tasks (eg, medical diagnosis
and face recognition) due to the effective extraction of discriminant features via reducing …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Real-time safety assessment (RTSA) of dynamic systems holds substantial implications
across diverse fields, including industrial and electronic applications. However, the …

In recent years, computational advancements have contributed to a significant increase in
the volume and availability of multivariate time series data (MTSD). The ability to make use …

Recently, online learning with imbalanced data streams has aroused wide concern, which
reflects an uneven distribution of different classes in data streams. Existing approaches have …

Online learning from the data streams is a research hotspot due to adaptive responses to
real-time data arrival and fleeting. Existing approaches can only handle real-world scenarios …

Data streams, as an important pattern of big data, require online real-time processing
because instances arrive one by one and are fleeting. Existing online learning methods …

Online learning is critical for handling complex data streams in Big Data-related
applications. This study explores a new online learning problem where both the features …

Data streams refer to data sequences generated at a high rate over a continuous period,
such as social media analysis, financial transaction monitoring, and sensor data processing …

Online streaming feature selection (OSFS), as an online learning manner to handle
streaming features, is critical in addressing high‐dimensional data. In real big data‐related …