Anomaly Detection Algorithm

Anomaly detection algorithms aim to identify data points deviating significantly from established norms, a crucial task across diverse fields like astronomy, industrial monitoring, and cybersecurity. Current research emphasizes unsupervised methods, particularly Isolation Forests and variations of LSTM networks, as well as classifier-based approaches that leverage the latent space of neural networks for improved anomaly scoring. These advancements are driving improvements in real-time anomaly detection across various data types (including time series and images), enhancing system reliability and enabling more effective decision-making in safety-critical applications.

Papers

June 6, 2022

Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms
Zhen-Yu Zhang, Guo-Xiang Shao, Chun-Ming Qiu, Yue-Jie Hou, En-Ming Zhao, Chi-Chun Zhou
Anomaly Detection Detecting Anomaly Anomaly Detection Algorithm Stable Bagging Early Disease Detection

May 24, 2022

MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection
Sourav Chatterjee, Rohan Bopardikar, Marius Guerard, Uttam Thakore, Xiaodong Jiang
Time Series Model Selection Online autoML Detection Algorithm Anomaly Detection Algorithm Parameter Tuning

April 24, 2022

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
Yajie Cui, Zhaoxiang Liu, Shiguo Lian
Timely Survey Anomaly Detection Unsupervised Anomaly Detection Industrial Anomaly Detection Surface Defect Detection Anomaly Detection Algorithm Industrial Image

March 21, 2022

ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets
Xiayu Liang, Ying Gao, Shanrong Xu
Ensemble Learning Imbalanced Datasets Anomaly Detection Algorithm

January 25, 2022

Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection
Hamza Bodor, Thai V. Hoang, Zonghua Zhang
Anomaly Detection Active Learning Unsupervised Anomaly Detection Qualitative Difference Anomaly Detection Algorithm Unsupervised Time Series Anomaly Detection Little Help

December 13, 2021

Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection
Nirmal Sobha Kartha, Clément Gautrais, Vincent Vercruyssen
Anomaly Detection Inherent Interpretability Anomaly Detection Algorithm Isolation Forest Isolation Based Anomaly

Anomaly Detection Algorithm

Papers

Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms

MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection