Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model

11 February 2021

Papers citing "Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model"

8 / 8 papers shown

Title
Thwarting Cybersecurity Attacks with Explainable Concept Drift I. Shaer Abdallah Shami AAML 61 3 0 18 Mar 2024
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective Jeng-Lin Li Chih-Fan Hsu Ming-Ching Chang Wei-Chao Chen OOD 110 2 0 20 Feb 2024
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification Enshui Yu Jie Lu Bin Zhang Guangquan Zhang 66 21 0 17 Dec 2023
Tracking changes using Kullback-Leibler divergence for the continual learning Sebastian Basterrech Michal Wo'zniak 67 13 0 10 Oct 2022
Change Detection for Local Explainability in Evolving Data Streams Johannes Haug Alexander Braun Stefan Zurn Gjergji Kasneci FAtt 40 10 0 06 Sep 2022
Learn to Adapt: Robust Drift Detection in Security Domain Aditya Kuppa Nhien-An Le-Khac OOD 50 18 0 15 Jun 2022
Passive learning to address nonstationarity in virtual flow metering applications M. Hotvedt B. Grimstad Lars Imsland 56 9 0 07 Feb 2022
Active Weighted Aging Ensemble for Drifted Data Stream Classification Michal Wo'zniak P. Zyblewski Pawel Ksieniewicz AI4TS 61 22 0 19 Dec 2021