Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …

Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

Learning expressive low-dimensional representations of ultrahigh-dimensional data, eg,
data with thousands/millions of features, has been a major way to enable learning methods …

Anomaly or outlier detection is consider as one of the vital application of data mining, which
deals with anomalies or outliers. Anomalies are considered as data points that are …

We consider the problem of anomaly detection with a small set of partially labeled anomaly
examples and a large-scale unlabeled dataset. This is a common scenario in many …

Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and
unsupervised learning problems. In this paper we are concerned with investigating the …

The first successful isolation‐based anomaly detector, ie, iForest, uses trees as a means to
perform isolation. Although it has been shown to have advantages over existing anomaly …

Blockchain technology has rapidly gained popularity, permeating various fields due to its
inherent features of security, transparency, and decentralization. Blockchain-based …