A Flexible Framework for Anomaly Detection via Dimensionality Reduction

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Authors Alireza Vafaei Sadr, Martin Kunz, Bruce A. Bassett
Journal/Conference Name 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Paper Category
Paper Abstract Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.
Date of publication 2019
Code Programming Language Jupyter Notebook
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