Evolving novelty detectors for specific applications
Neurocomputing
Anomaly Detection Using Time Index Differences of Identical Symbols with and without Training Data
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Online heterogeneous mixture modeling with marginal and copula selection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes of clustering structures using normalized maximum likelihood coding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable structural break detection
Applied Soft Computing
| Hi-index | 754.84 |
We are concerned with the issue of dynamically selecting optimal statistical models from time series. The goal is not to select a single optimal model over the data as in conventional model selection, but to select a time series of optimal models under the assumption that the data source may be nonstationary. We call this issue dynamic model selection (DMS). From the standpoint of minimum description length principle, we first propose coding-theoretic criteria for DMS. Next, we propose efficient DMS algorithms on the basis of the criteria and analyze their performance in terms of their total code lengths and computation time. Finally, we apply DMS to novelty detection and demonstrate its effectiveness through empirical results on masquerade detection using UNIX command sequences.