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Cluster-Based Improved Isolation Forest [PDF]

Entropy, 2022
Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier ...
Chen Shao, Xusheng Du, Jiong Yu, Jiaying Chen   +3 more
doaj   +4 more sources

Deep Isolation Forest for Anomaly Detection [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2022
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability.
Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang   +3 more
semanticscholar   +4 more sources

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus. [PDF]

PLoS One
Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly.
Idris NF, Ismail MA, Jaya MIM, Ibrahim AO, Abulfaraj AW, Binzagr F.   +5 more
europepmc   +2 more sources

An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest

Applied Sciences, 2023
With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment.
Junxiang Chen, Jilin Zhang, Ruixiang Qian, Junfeng Yuan, Yongjian Ren   +4 more
doaj   +2 more sources

Detecting anomalies using rotated isolation forest

Data Mining and Knowledge Discovery
The Isolation Forest (iForest), proposed by Liu et al. at TKDE 2012, has become a prominent tool for unsupervised anomaly detection. However, recent research by Hariri, Kind, and Brunner, published in TKDE 2021, has revealed issues with iForest.
Vahideh Monemizadeh, Kourosh Kiani
semanticscholar   +3 more sources

Generalized isolation forest for anomaly detection [PDF]

Pattern Recognition Letters, 2021
This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (EIF). EIF has shown some interest compared to IF being for instance more robust to some artefacts.
Julien Lesouple, C. Baudoin, M. Spigai, J. Tourneret   +3 more
semanticscholar   +4 more sources

Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms

Minerals
Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between ...
Mobin Saremi, A. Hezarkhani, Seyyed Ataollah Agha Seyyed Mirzabozorg, Ramin DehghanNiri, Adel Shirazy, A. Shirazi   +5 more
semanticscholar   +2 more sources

Isolation Forest With Exclusion of Attributes Based on Shapley Index

IEEE Access
Recognizing anomalies is an extremely important process in data analysis, aimed at identifying patterns in data that deviate from known norms or typical standards.
Albert Rachwal, Pawel Karczmarek, Alicja Rachwal, Rafal Stegierski   +3 more
doaj   +2 more sources

Online Isolation Forest

International Conference on Machine Learning
The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming context.
Filippo Leveni, G. Cassales, Bernhard Pfahringer, A. Bifet, Giacomo Boracchi   +4 more
semanticscholar   +3 more sources

Impact of Data Distribution and Bootstrap Setting on Anomaly Detection Using Isolation Forest in Process Quality Control [PDF]

Entropy
This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. Although iForest has received attention for its multivariate
Hyunyul Choi, Kihyo Jung
doaj   +2 more sources