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Deep Isolation Forest for Anomaly Detection
IEEE Transactions on Knowledge and Data Engineering, 2023 Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)
Hongzuo Xu, Guansong Pang, Yongjun Wang
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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.
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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.
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K-Means-based isolation forest
Knowledge-Based Systems, 2020 Abstract The task of anomaly detection in data is one of the main challenges in data science because of the wide plethora of applications and despite a spectrum of available methods. Unfortunately, many of anomaly detection schemes are still imperfect i.e., they are not effective enough or act in a non-intuitive way or they are focused on a specific ...
Adam Kiersztyn, Paweł Karczmarek
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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 +2 more
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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 +2 more
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Enhanced anomaly scores for isolation forests
Pattern Recognition, 2021Abstract Isolation Forest represents a variant of Random Forest largely and successfully employed for outlier detection. The main idea is that outliers are likely to get isolated in a tree after few splits. The anomaly score is therefore a function inversely related to the leaf depth.
Antonella Mensi, Manuele Bicego
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Fuzzy Set-Based Isolation Forest
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020One of the main challenges is the analysis of large data sets, in particular those containing various types of data, such as time, place, image, and those assuming categorical values. This type of data may contain numerous outliers. Despite the continuous development of data analysis, many methods can be effectively improved, in particular through the ...
Pawel Karczmarek +2 more
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Layered isolation forest: A multi-level subspace algorithm for improving isolation forest
NeurocomputingTao Liu, Zhen Zhou, Lijun Yang
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Response to fire by a forest specialist in isolated montane forest
Forest Ecology and Management, 2020Abstract Fire events have increased in scale and severity due to hotter, drier conditions brought on by climate change and fire suppression. Extreme fire events can be detrimental to forest specialists, especially populations at the edge of the species range, where conditions can vary from those within the core of the range. The San Bernardino flying
Maxwell N. Mazzella, John L. Koprowski
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