Search Results for author:
Found 6 papers, 5 papers with code
Incorporating Feedback into Tree-based Anomaly Detection
Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.
Active Anomaly Detection via Ensembles
First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors.
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning
Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
A Meta-Analysis of the Anomaly Detection Problem
The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.
Active Anomaly Detection for time-domain discoveries
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets.
