With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

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 …

We present a novel deep-learning-based approach for one-class transfer learning in which
labeled data from an unrelated task is used for feature learning in one-class classification …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Training a machine learning algorithm on a class-imbalanced dataset can be a difficult task,
a process that could prove even more challenging under conditions of high dimensionality …

Many online applications, such as online social networks or knowledge bases, are often
attacked by malicious users who commit different types of actions such as vandalism on …

Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is
no exception. Robotic process automation (RPA) is taking over manual tasks in TI business …

The majority of patients in the hospital are ambulatory and would benefit significantly from
predictive and personalized monitoring systems. Such patients are well suited to having …

In this paper, we propose DiFF-RF, an ensemble approach composed of random partitioning
binary trees to detect point-wise and collective (as well as contextual) anomalies. Thanks to …

Time series anomaly detection is instrumental in maintaining system availability in various
domains. Current work in this research line mainly focuses on learning data normality …