Results 1 to 10 of about 81,945 (154)
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 +3 more
doaj +6 more sources
Extended Isolation Forest [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 12 pages; 21 figures, Published.
Matias Carrasco Kind +2 more
exaly +3 more sources
Generalized isolation forest for anomaly detection [PDF]
Pattern Recognition Letters, 2021 Abstract 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. However, some information can be lost when computing the EIF trees since the sampled threshold might lead to empty branches.
Jean-Yves Tourneret, Julien Lesouple
exaly +4 more sources
A probabilistic generalization of isolation forest
Information Sciences, 2022 Abstract The problem of finding anomalies and outliers in datasets is one of the most important challenges of modern data analysis. Among the commonly dedicated tools to solve this task one can find Isolation Forest (IF) that is an efficient, conceptually simple, and fast method. In this study, we propose the Probabilistic Generalization of Isolation
Paweł Karczmarek
exaly +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 +4 more
doaj +3 more sources
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
exaly +4 more sources
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
exaly +2 more sources
Impact of Data Distribution and Bootstrap Setting on Anomaly Detection Using Isolation Forest in Process Quality Control [PDF]
EntropyThis 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
An In-Depth Study and Improvement of Isolation Forest [PDF]
IEEE Access, 2022 Historically, anomalies detection was an important issue for industrial applications such as the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the ever increasing demand in different fields such as intrusion detection, fraud detection, ecosystem change detection or event detection in sensor networks ...
Yousra Chabchoub +3 more
openaire +3 more sources
Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
Sensors, 2021 In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not ...
Donghyun Kim +4 more
doaj +1 more source