A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

被引：0

作者：

Jacob, Vincent ^{[1
]}

Song, Fei ^{[1
]}

Stiegler, Arnaud ^{[1
]}

Rad, Bijan ^{[1
]}

Diao, Yanlei ^{[1
]}

Tatbul, Nesime ^{[2
,3
]}

机构：

[1] Ecole Polytech, Palaiseau, France

[2] Intel Labs, New York, NY USA

[3] MIT, Cambridge, MA 02139 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2021年 / 14卷 / 12期

基金：

欧洲研究理事会;

关键词：

D O I：

10.14778/3476311.3476355

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this demo, we introduce Exathlon - a new benchmarking platform for explainable anomaly detection over high-dimensional time series. We designed Exathlon to support data scientists and researchers in developing and evaluating learned models and algorithms for detecting anomalous patterns as well as discovering their explanations. This demo will showcase Exathlon's curated anomaly dataset, novel benchmarking methodology, and end-to-end data science pipeline in action via example usage scenarios.

引用

页码：2827 / 2830

页数：4

共 50 条

[41] Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations
Park, Sungwoo
Moon, Jihoon
Hwang, Eenjun
20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2020), 2020, : 762 - 765
[42] Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems
Abououf, Menatalla
Singh, Shakti
Mizouni, Rabeb
Otrok, Hadi
IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (02) : 3446 - 3457
[43] A General-Purpose Method for Applying Explainable AI for Anomaly Detection
Sipple, John
Youssef, Abdou
FOUNDATIONS OF INTELLIGENT SYSTEMS (ISMIS 2022), 2022, 13515 : 162 - 174
[44] Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
Kim, Donghyun
Antariksa, Gian
Handayani, Melia Putri
Lee, Sangbong
Lee, Jihwan
SENSORS, 2021, 21 (15)
[45] An Explainable Machine Learning Approach for Anomaly Detection in Satellite Telemetry Data
Kricheff, Seth
Maxwell, Emily
Plaks, Connor
Simon, Michelle
2024 IEEE AEROSPACE CONFERENCE, 2024,
[46] Explainable Unsupervised Multi-Sensor Industrial Anomaly Detection and Categorization
Ameli, Mina
Becker, Philipp Aaron
Lankers, Katharina
van Ackeren, Markus
Baehring, Holger
Maass, Wolfgang
2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, : 1468 - 1475
[47] Explainable Anomaly Detection System for Categorical Sensor Data in Internet of Things
Yuan, Peng
Tang, Lu-An
Chen, Haifeng
Sato, Moto
Woodward, Kevin
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT VI, 2023, 13718 : 594 - 598
[48] A Real-time Explainable Anomaly Detection System for Connected Vehicles
Nguyen, Duc Cuong
Nguyen, Kien Dang
Chacko, Simy
PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY (IOTBDS), 2022, : 17 - 25
[49] Explainable correlation-based anomaly detection for Industrial Control Systems
Birihanu, Ermiyas
Lendak, Imre
FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2025, 7
[50] SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection
Naser, M. Z.
Naser, Ahmad Z.
JOURNAL OF BIG DATA, 2025, 12 (01)

← 1 2 3 4 5 →