Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Multilabel classification as a data mining task has recently attracted increasing interest from
researchers. Many current data mining applications address problems with instances that …

Multi-label classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …

scikit-multilearn is a Python library for performing multi-label classification. The library is
compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

Fault diagnosis of marine machinery is of utmost importance in modern ships. The widely
used machine learning techniques have made it possible to realize intelligent diagnosis by …

Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …

Multi-view learning (MVL) is an active direction in machine learning that aims at exploiting
the consensus and complementarity information among multiple distinct feature sets to boost …

Semi-supervised learning has become a hot learning framework, where large amounts of
unlabeled data and small amounts of labeled data are available during the training process …

Electrocardiogram (ECG) has been proved to be the most common and effective approach
to investigate the cardiovascular disease because that it is simple, non-invasive and low …