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Proximity Isolation Forests

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

Adaptive Isolation Forest

IFIP Working Conference on Database Semantics
Jia Justin Liu   +4 more
openaire   +2 more sources

Paenibacillus caseinilyticus sp. nov., isolated forest soil

International Journal of Systematic and Evolutionary Microbiology, 2023
A milky-white-coloured, aerobic, Gram-stain-positive, rod-shaped and motile bacterial strain (GW78T) was isolated from forest soil. GW78T was catalase-positive and oxidase-negative. The strain was able to grow optimally at 37 °C and at pH 7.0 in Reasoner's 2A media.
Hyosun Lee   +6 more
openaire   +2 more sources

Fuzzy Set-Based Isolation Forest

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
One 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
openaire   +1 more source

Two-Stream Isolation Forest Based on Deep Features for Hyperspectral Anomaly Detection

IEEE Geoscience and Remote Sensing Letters, 2023
Hyperspectral anomaly detection (HAD) is a challenging task in hyperspectral image processing, which is to capture the anomaly by spectral and spatial information without prior knowledge.
Xi Cheng   +5 more
semanticscholar   +1 more source

Enhanced anomaly scores for isolation forests

Pattern Recognition, 2021
Abstract 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
openaire   +2 more sources

Advancing Patient Care for Autonomous Saline Level Alert System with Cloud-Based Isolation Forest Algorithm

International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]
This work presents a novel method for improving patient care by creating a self-sufficient system to detect saline levels using an Isolation Forest Algorithm (IFA) hosted on the cloud.
N. R   +5 more
semanticscholar   +1 more source

Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-Based Power Line Inspection

2024 2nd International Conference on Networking and Communications (ICNWC)
Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security.
Jayabharathi Ramasamy   +5 more
semanticscholar   +1 more source

Isolation Forest Based Anomaly Detection and Fault Localization for Solar PV System

2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2023
The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source.
S. Kabir   +2 more
semanticscholar   +1 more source

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