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2008 Eighth IEEE International Conference on Data Mining, 2008
Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points.
Fei Tony Liu, K. Ting, Zhi-Hua Zhou
semanticscholar +2 more sources
Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points.
Fei Tony Liu, K. Ting, Zhi-Hua Zhou
semanticscholar +2 more sources
Density-based isolation forest
2025 IEEE Statistical Signal Processing Workshop (SSP)Nathan Levêque +5 more
semanticscholar +2 more sources
2020 25th International Conference on Pattern Recognition (ICPR), 2021
Isolation Forests are a very successful approach for solving outlier detection tasks. Isolation Forests are based on classical Random Forest classifiers that require feature vectors as input. There are many situations where vectorial data is not readily available, for instance when dealing with input sequences or strings.
Mensi, A., Bicego, M., Tax, D. M. J.
openaire +2 more sources
Isolation Forests are a very successful approach for solving outlier detection tasks. Isolation Forests are based on classical Random Forest classifiers that require feature vectors as input. There are many situations where vectorial data is not readily available, for instance when dealing with input sequences or strings.
Mensi, A., Bicego, M., Tax, D. M. J.
openaire +2 more sources
Layered isolation forest: A multi-level subspace algorithm for improving isolation forest
NeurocomputingTao Liu, Zheng-Yang Zhou, Lijun Yang
semanticscholar +2 more sources