Graduate Thesis Or Dissertation

 

A Benchmarking Study of Unsupervised Anomaly Detection Algorithms Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/sn00b4999

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  • It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of anomalies --- and then proposing how to simulate and control these factors when sampling points during benchmark creation. We apply this methodology to produce a large corpus of unsupervised anomaly detection benchmarks and then evaluate several state-of-the-art anomaly detection algorithms against this corpus. Our final analysis not only compares the performance of these algorithms across a large variety of problems, but it also assesses the impact of our identified problems dimensions on experimental outcomes.
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