Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Over the recent years, the growth of online social media has greatly facilitated the way
people communicate with each other. Users of online social media share information …

The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

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 …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Novelty detection is the process of identifying the observation (s) that differ in some respect
from the training observations (the target class). In reality, the novelty class is often absent …

In e-commerce, user reviews can play a significant role in determining the revenue of an
organisation. Online users rely on reviews before making decisions about any product and …

Novelty detection is the problem of identifying whether a new data point is considered to be
an inlier or an outlier. We assume that training data is available to describe only the inlier …

As a vital topic in media content interpretation, video anomaly detection (VAD) has made
fruitful progress via deep neural network (DNN). However, existing methods usually follow a …