Anomaly Detection, Novelty Detection, Out-of-Distribution Detection
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PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge.
We present results and analysis for a wide range of algorithms on this benchmark, and discuss future challenges for the emerging field of streaming analytics.
Ranked #1 on Anomaly Detection on Numenta Anomaly Benchmark
We present a novel algorithm for anomaly detection on very large datasets and data streams.