Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble

A heterogeneous online learning ensemble for non-stationary environments

(2019) Mobin M. Idrees et al.   KNOWLEDGE-BASED SYSTEMS

Machine learning based concept drift detection for predictive maintenance

(2019) Jan Zenisek et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Kappa Updated Ensemble for drifting data stream mining

(2019) Alberto Cano et al.   MACHINE LEARNING

Imbalance learning using heterogeneous ensembles

(2019) Hossein Ghaderi Zefrehi et al.   EXPERT SYSTEMS WITH APPLICATIONS

Concept drift in e-mail datasets: An empirical study with practical implications

(2018) David Ruano-Ordás et al.   INFORMATION SCIENCES

Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams

(2018) Ali Pesaranghader et al.   MACHINE LEARNING

The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift

(2018) Siqi Ren et al.   NEUROCOMPUTING

A Systematic Study of Online Class Imbalance Learning With Concept Drift

(2018) Shuo Wang et al.   IEEE Transactions on Neural Networks and Learning Systems

An iterative boosting-based ensemble for streaming data classification

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

Ensemble learning for data stream analysis: A survey

(2017) Bartosz Krawczyk et al.   Information Fusion

Dynamic adaptation of online ensembles for drifting data streams

(2017) M. Kehinde Olorunnimbe et al.   JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

(2017) Jean Paul Barddal et al.   JOURNAL OF SYSTEMS AND SOFTWARE

The online performance estimation framework: heterogeneous ensemble learning for data streams

(2017) Jan N. van Rijn et al.   MACHINE LEARNING

Adaptive random forests for evolving data stream classification

(2017) Heitor M. Gomes et al.   MACHINE LEARNING

Exploiting concept drift to predict popularity of social multimedia in microblogs

(2016) Cheng-Te Li et al.   INFORMATION SCIENCES

Learning in Nonstationary Environments: A Survey

(2015) Gregory Ditzler et al.   IEEE Computational Intelligence Magazine

A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority

(2015) Parneeta Sidhu et al.   International Journal of Machine Learning and Cybernetics

A survey on concept drift adaptation

(2014) João Gama et al.   ACM COMPUTING SURVEYS

A comparative study on concept drift detectors

(2014) Paulo M. Gonçalves et al.   EXPERT SYSTEMS WITH APPLICATIONS

Prediction of members’ return visit rates using a time factor

(2013) Rui-Dong Chiang et al.   Electronic Commerce Research and Applications

Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers

(2013) Rafal Lysiak et al.   NEUROCOMPUTING

The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

(2009) L.L. Minku et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING