Abstract
Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods.
Original language | English |
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Title of host publication | Proceedings of the 26th International Conference on Scientific and Statistical Database Management |
Number of pages | 12 |
Publisher | Association for Computing Machinery |
Publication date | 2014 |
Article number | 13 |
ISBN (Print) | 978-1-4503-2722-0 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | International Conference on Scientific and Statistical Database Management - Aalborg, Denmark Duration: 30. Jun 2014 → 2. Jul 2014 |
Conference
Conference | International Conference on Scientific and Statistical Database Management |
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Country/Territory | Denmark |
City | Aalborg |
Period | 30/06/2014 → 02/07/2014 |
Sponsor | Danish Otto Monsted Foundation, TARGIT |
Keywords
- Ensemble
- Outlier detection