Conventional supervised binary classification algorithms have been widely applied to
address significant research questions using biological and biomedical data. This …

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 …

Background As an important type of post-translational modification (PTM), protein
glycosylation plays a crucial role in protein stability and protein function. The abundance …

Training with confident positive-labeled instances has received a lot of attention in Positive
and Unlabeled (PU) learning tasks, and this is formally termed “Instance-Dependent PU …

Aerators are essential and crucial auxiliary devices in intensive culture, especially in
industrial culture in China. In this paper, we propose a real-time expert system for anomaly …

The current state-of-art for tackling the problem of classification of static uncertain data under
PU learning (Positive Unlabeled Learning) scenario, is UPNB. It is based on the Bayesian …

The rapid growth in the number of networked applications that naturally generate complex
text data, which contains not only inner features but also inter-dependent relations, has …

For the positive and unlabeled learning algorithms, when there is only small amount of
labeled positive examples available, the algorithms can hardly extract reliable negative …

Aerators are essential and crucial auxiliary devices in intensive culture, especially in
industrial culture in China. The traditional methods cannot accurately detect abnormal …

The independence assumptions help Bayesian network classifier (BNC), eg, Naive Bayes
(NB), reduce structure complexity and perform surprisingly well in many real-world …