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Anomaly detection by combining decision trees and parametric densities
2008
Pattern Recognition (ICPR), Proceedings of the International Conference on
In this paper a modified decision tree algorithm for anomaly detection is presented. During the tree building process, densities for the outlier class are used directly in the split point determination algorithm. No artificial counter-examples have to be sampled from the unknown class, which yields to more precise decision boundaries and a deterministic classification result. Furthermore, the prior of the outlier class can be used to adjust the sensitivity of the anomaly detector. The proposed
doi:10.1109/icpr.2008.4761796
dblp:conf/icpr/ReifGSB08
fatcat:33ryubejrzf5llky4qkjait4wy