An ensemble-based semi-supervised learning approach for non-stationary imbalanced data streams with label scarcity

This publication has 47 references indexed in Scilit:

• General Performance Score for classification problems
  Applied Intelligence, 2022
• Learning from evolving data streams through ensembles of random patches
  Knowledge and Information Systems, 2021
• A survey on semi-supervised learning
  Machine Learning, 2019
• Data stream mining: methods and challenges for handling concept drift
  SN Applied Sciences, 2019
• Kappa Updated Ensemble for drifting data stream mining
  Machine Learning, 2019
• Online ensemble learning with abstaining classifiers for drifting and noisy data streams
  Applied Soft Computing, 2018
• Improving semi-supervised co-forest algorithm in evolving data streams
  Applied Intelligence, 2018
• Supporting the Page-Hinkley Test with Empirical Mode Decomposition for Change Detection
  2017
• An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams
  Knowledge and Information Systems, 2015
• The ClusTree: indexing micro-clusters for anytime stream mining
  Knowledge and Information Systems, 2010