Heterogeneous ensemble selection for evolving data streams

Multi-label classification via label correlation and first order feature dependance in a data stream

(2019) Tien Thanh Nguyen et al.   PATTERN RECOGNITION

A heterogeneous online learning ensemble for non-stationary environments

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

Multi-label classification via incremental clustering on an evolving data stream

(2019) Tien Thanh Nguyen et al.   PATTERN RECOGNITION

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

(2018) Bartosz Krawczyk et al.   APPLIED SOFT COMPUTING

A novel heterogeneous ensemble credit scoring model based on bstacking approach

(2018) Yufei Xia et al.   EXPERT SYSTEMS WITH APPLICATIONS

Heterogeneous classifier ensemble with fuzzy rule-based meta learner

(2018) Tien Thanh Nguyen et al.   INFORMATION SCIENCES

Dynamic ensemble selection for multi-class classification with one-class classifiers

(2018) Bartosz Krawczyk et al.   PATTERN RECOGNITION

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

(2018) Rafael M.O. Cruz et al.   PATTERN RECOGNITION

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

Multi-label classification via multi-target regression on data streams

(2016) Aljaž Osojnik et al.   MACHINE LEARNING

META-DES: A dynamic ensemble selection framework using meta-learning

(2015) Rafael M.O. Cruz et al.   PATTERN RECOGNITION

On evaluating stream learning algorithms

(2012) João Gama et al.   MACHINE LEARNING

Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition

(2011) Jean-François Connolly et al.   PATTERN RECOGNITION