Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

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 …

The input to an algorithm that learns a binary classifier normally consists of two sets of
examples, where one set consists of positive examples of the concept to be learned, and the …

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive
examples with many unlabeled ones. Compared with ordinary semi-supervised learning …

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 …

In this paper, we present a comprehensive review of recent developments in the application
of machine learning algorithms to Spam filtering, focusing on both textual-and image-based …

Classification of Web page content is essential to many tasks in Web information retrieval
such as maintaining Web directories and focused crawling. The uncontrolled nature of Web …

We consider the problem of learning a binary classifier from a training set of positive and
unlabeled examples, both in the inductive and in the transductive setting. This problem …

One way to describe anomalies is by saying that anomalies are not concentrated. This leads
to the problem of finding level sets for the data generating density. We interpret this learning …