Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …

One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU
learning, in which the state of the art is unbiased PU learning. However, if its model is very …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

The fast spreading of fake news stories on social media can cause inestimable social harm.
Develo** effective methods to detect them early is of paramount importance. A major …

In the Social Web scenario, where large amounts of User Generated Content diffuse through
Social Media, the risk of running into misinformation is not negligible. For this reason …

Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …