5 research outputs found
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Semi-supervised anomaly detection methods leverage a few anomaly examples to
yield drastically improved performance compared to unsupervised models.
However, they still suffer from two limitations: 1) unlabeled anomalies (i.e.,
anomaly contamination) may mislead the learning process when all the unlabeled
data are employed as inliers for model training; 2) only discrete supervision
information (such as binary or ordinal data labels) is exploited, which leads
to suboptimal learning of anomaly scores that essentially take on a continuous
distribution. Therefore, this paper proposes a novel semi-supervised anomaly
detection method, which devises \textit{contamination-resilient continuous
supervisory signals}. Specifically, we propose a mass interpolation method to
diffuse the abnormality of labeled anomalies, thereby creating new data samples
labeled with continuous abnormal degrees. Meanwhile, the contaminated area can
be covered by new data samples generated via combinations of data with correct
labels. A feature learning-based objective is added to serve as an optimization
constraint to regularize the network and further enhance the robustness w.r.t.
anomaly contamination. Extensive experiments on 11 real-world datasets show
that our approach significantly outperforms state-of-the-art competitors by
20%-30% in AUC-PR and obtains more robust and superior performance in settings
with different anomaly contamination levels and varying numbers of labeled
anomalies. The source code is available at https://github.com/xuhongzuo/rosas/.Comment: Accepted by Information Processing and Management (IP&M
Selective value coupling learning for detecting outliers in high-dimensional categorical data
Deep Isolation Forest for Anomaly Detection
Isolation forest (iForest) has been emerging as arguably the most popular
anomaly detector in recent years. It iteratively performs axis-parallel data
space partition in a tree structure to isolate deviated data objects from the
other data, with the isolation difficulty of the objects defined as anomaly
scores. iForest shows effective performance across popular dataset benchmarks,
but its axis-parallel-based linear data partition is ineffective in handling
hard anomalies in high-dimensional/non-linear-separable data space, and even
worse, it leads to a notorious algorithmic bias that assigns unexpectedly large
anomaly scores to artefact regions. There have been several extensions of
iForest, but they still focus on linear data partition, failing to effectively
isolate those hard anomalies. This paper introduces a novel extension of
iForest, deep isolation forest. Our method offers a comprehensive isolation
method that can arbitrarily partition the data at any random direction and
angle on subspaces of any size, effectively avoiding the algorithmic bias in
the linear partition. Further, it requires only randomly initialised neural
networks (i.e., no optimisation is required in our method) to ensure the
freedom of the partition. In doing so, desired randomness and diversity in both
random network-based representations and random partition-based isolation can
be fully leveraged to significantly enhance the isolation ensemble-based
anomaly detection. Also, our approach offers a data-type-agnostic anomaly
detection solution. It is versatile to detect anomalies in different types of
data by simply plugging in corresponding randomly initialised neural networks
in the feature mapping. Extensive empirical results on a large collection of
real-world datasets show that our model achieves substantial improvement over
state-of-the-art isolation-based and non-isolation-based anomaly detection
models.Comment: submission of KDD 2022, 12 pages, 7 figures; the source code is
released at https://github.com/xuhongzuo/deep-ifores