Accuracy weighted diversity-based online boosting

Kappa Updated Ensemble for drifting data stream mining

(2019) Alberto Cano et al.   MACHINE LEARNING

An overview and comprehensive comparison of ensembles for concept drift

(2019) Roberto Souto Maior de Barros et al.   Information Fusion

DRED: An evolutionary diversity generation method for concept drift adaptation in online learning environments

(2018) Jesus L. Lobo et al.   APPLIED SOFT COMPUTING

Online ensemble learning with abstaining classifiers for drifting and noisy data streams

(2018) Bartosz Krawczyk et al.   APPLIED SOFT COMPUTING

Reliable writer identification in medieval manuscripts through page layout features: The “Avila” Bible case

(2018) C. De Stefano et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

A large-scale comparison of concept drift detectors

(2018) Roberto Souto Maior Barros et al.   INFORMATION SCIENCES

Knowledge-maximized ensemble algorithm for different types of concept drift

(2018) Siqi Ren et al.   INFORMATION SCIENCES

Concept Drift Adaptation by Exploiting Historical Knowledge

(2018) Yu Sun et al.   IEEE Transactions on Neural Networks and Learning Systems

Adaptive learning on mobile network traffic data

(2018) Zhen Liu et al.   CONNECTION SCIENCE

An iterative boosting-based ensemble for streaming data classification

(2018) João Roberto Bertini Junior et al.   Information Fusion

RDDM: Reactive drift detection method

(2017) Roberto S.M. Barros et al.   EXPERT SYSTEMS WITH APPLICATIONS

A new dynamic modeling framework for credit risk assessment

(2016) Maria Rocha Sousa et al.   EXPERT SYSTEMS WITH APPLICATIONS

Online Ensemble Learning of Data Streams with Gradually Evolved Classes

(2016) Yu Sun et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds

(2015) Isvani Frias-Blanco et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A survey on concept drift adaptation

(2014) João Gama et al.   ACM COMPUTING SURVEYS

On evaluating stream learning algorithms

(2012) João Gama et al.   MACHINE LEARNING

DDD: A New Ensemble Approach for Dealing with Concept Drift

(2011) Leandro L. Minku et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING