Feb. 13, 2024, 5:42 a.m. | Santonu Sarkar Shanay Mehta Nicole Fernandes Jyotirmoy Sarkar Snehanshu Saha

cs.LG updates on arXiv.org arxiv.org

Detection of anomalous situations for complex mission-critical systems holds paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of machine learning-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine …

anomaly anomaly detection benchmarking challenge class continuity cs.lg data deep learning detection distribution events importance major mission service study systems tree

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Director of Machine Learning

@ Axelera AI | Hybrid/Remote - Europe (incl. UK)

Senior Data Scientist - Trendyol Milla

@ Trendyol | Istanbul (All)

Data Scientist, Mid

@ Booz Allen Hamilton | USA, CA, San Diego (1615 Murray Canyon Rd)

Systems Development Engineer , Amazon Robotics Business Applications and Solutions Engineering

@ Amazon.com | Boston, Massachusetts, USA