Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Text Categorization is a pattern classification task for text mining and necessary for efficient
management of textual information systems. The documents can be classified by three ways …

Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC)
systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC …

Simultaneous-fault diagnosis is a common problem in many applications and well-studied
for time-independent patterns. However, most practical applications are of the type of time …

In order to reduce operation and maintenance costs, reliability, and quick response
capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more …

Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-
directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance …

In multi-label classification problems, every instance is associated with multiple labels at the
same time. Binary classification, multi-class classification and ordinal regression problems …

Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the
maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal …

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is
complicated and inherent with many types of component faults, is essential to avoid the …