Anomaly detection based on improved isolated forest-Reference-Cited by-同舟云学术

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Anomaly detection based on improved isolated forest

Published:2023-05-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)
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Author:

Cheng Xinyun¹,Liu Zhe¹,Tang Ming¹,Du Yuanhan¹,Xu Chenwei¹

Affiliation:

1. Information and Communication Branch of State Grid Jiangsu Electric Power Co., Ltd.,Nanjing,China

Funder

State Grid Jiangsu Electric Power

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164832/10164812/10165081.pdf?arnumber=10165081

Reference16 articles.

1. Fixing the Train-test Objective Discrepancy: Iterative Image Inpainting for Unsupervised Anomaly Detection[J];hitoshi;Journal of Information Processing,2022

2. Network Intrusion Detection System Using 2D Anomaly Detection[J];kim;IEICE Proceeding Series,2022

3. Real time outlier monitoring for power transformer fault diagnosis based on isolated forest[J];li;IOP Conference Series: Materials Science and Engineering,2020

4. Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study[J];trindade;Applied Sciences,2021

5. Mahalanobis-Taguchi Method for Anomaly Detection and Classification[J];kentaro;Total Quality Science,2022

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1. Reactor Vibration Signal Processing Method Based on Isolated Forest and Fuzzy C-Means Algorithm;2023 5th International Conference on System Reliability and Safety Engineering (SRSE);2023-10-20

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