Abstract
The success of various methods for unsupervised outlier detection t depends on how well their definition of an outlier matches the dataset properties. Given that each definition, and hence each t method, has strengths and weaknesses, measuring those properties could help us to predict the most suitable method for the dataset at hand. In this paper, we construct and validate a set of meta-features that measure such properties. We then conduct the first instance space analysis for unsupervised outlier detection methods based on the meta-features. The analysis provides insights into the methods’ strengths and weaknesses, and facilitates the recommendation of an appropriate method with good accuracy. (
Original language | English |
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Title of host publication | Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning co-located with SIAM International Conference on Data Mining (SDM 2019) |
Editors | Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann |
Place of Publication | Germany |
Publisher | Rheinisch-Westfaelische Technische Hochschule Aachen |
Number of pages | 9 |
Publication status | Published - 2019 |
Event | Evaluation and Experimental Design in Data Mining and Machine Learning 2019 - Hyatt Regency Calgary, Calgary, Canada Duration: 4 May 2019 → … Conference number: 1st https://imada.sdu.dk/Research/EDML/ (Workshop website) http://ceur-ws.org/Vol-2436/ (Proceedings) |
Publication series
Name | CEUR Workshop Proceedings |
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Volume | 2436 |
ISSN (Print) | 1613-0073 |
Workshop
Workshop | Evaluation and Experimental Design in Data Mining and Machine Learning 2019 |
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Abbreviated title | EDML 2019 |
Country/Territory | Canada |
City | Calgary |
Period | 4/05/19 → … |
Other | Workshop at the SIAM International Conference on Data Mining (SDM19) |
Internet address |
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