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 …

Land-cover map** in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

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 …

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 …

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 …

Many applications of collaborative filtering (CF), such as news item recommendation and
bookmark recommendation, are most naturally thought of as one-class collaborative filtering …

In this paper we present methods of enhancing existing discriminative classifiers for multi-
labeled predictions. Discriminative methods like support vector machines perform very well …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

We study the problem of building text classifiers using positive and unlabeled examples. The
key feature of this problem is that there is no negative example for learning. Recently, a few …

Abstract The One Class Classification (OCC) problem is different from the conventional
binary/multi-class classification problem in the sense that in OCC, the negative class is …