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"

6 / 6 papers shown

Title
Challenges in Benchmarking Stream Learning Algorithms with Real-world Data Vinicius M. A. Souza Denis Moreira dos Reis André Gustavo Maletzke Gustavo E. A. P. A. Batista OOD AI4TS 69 131 0 30 Apr 2020
An Ensemble Generation Method Based on Instance Hardness Felipe N. Walmsley George D. C. Cavalcanti Dayvid V. R. Oliveira Rafael M. O. Cruz R. Sabourin 42 15 0 20 Apr 2018
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams Ali Pesaranghader H. Viktor E. Paquet 47 65 0 07 Sep 2017
A Systematic Study of Online Class Imbalance Learning with Concept Drift Shuo Wang Leandro L. Minku Xin Yao 44 257 0 20 Mar 2017
Characterizing Concept Drift Geoffrey I. Webb Roy Hyde Hong Cao Hai-Long Nguyen F. Petitjean 76 424 0 12 Nov 2015
Exponentially Weighted Moving Average Charts for Detecting Concept Drift Gordon J. Ross N. Adams D. Tasoulis D. Hand 85 370 0 25 Dec 2012