Ensemble Classification Methods for Imbalanced Data Streams
In real-world scenarios,learning from data streams often faces the challenge of class imbalance,where learning algorithms are unable to effectively recognize minority class samples due to the lack of training data.To introduce the cur-rent research status and challenges of ensemble classification for imbalanced data streams,recent literature in this field is reviewed.The analysis and summary are conducted from the perspectives of decision rules based on weighting,selection,and voting,as well as learning methods based on cost-sensitive learning,active learning,and incremental learning.The performance of algorithms using the same dataset is compared.To address the imbalance issues in different types of com-plex data streams,ensemble classification algorithms are summarized from four aspects:concept drift,multi-class,noise,and class overlap.The time complexity of classical algorithms is analyzed.Finally,the classification challenges of imbal-anced issues in dynamic data streams,data streams with missing information,multi-label data streams,and uncertain data streams are proposed for future research on ensemble strategies.
imbalanced data streamsensemble classificationdecision rulelearning methodscomplex data streams
万方数据
维普
