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 …

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 …

Growing interest in the potential applications of two-dimensional (2D) materials has fueled
advancement in the identification of 2D systems with exotic properties. Increasingly, the …

The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …

This paper surveys the existing method of learning from positive and unlabeled examples.
We divide the existing methods into three families, and review the main algorithms …

For tasks such as medical diagnosis and knowledge base completion, a classifier may only
have access to positive and unlabeled examples, where the unlabeled data consists of both …

Web classification has been attempted through many different technologies. In this study we
concentrate on the comparison of Neural Networks (NN), Naïve Bayes (NB) and Decision …

It is difficult to identify sentence importance from a single point of view. In this paper, we
propose a learning-based approach to combine various sentence features. They are …

Graph neural networks (GNNs) have demonstrated great success in many graph data-based
applications. The impressive behavior of GNNs typically relies on the availability of a …

This paper will address the Positive and Unlabeled learning problem (PU learning) and its
importance in the growing field of semi-supervised learning. In most real-world classification …