A classification approach for learning concept-drift in noisy data stream
Peipei Li · 2011
Classification of data streams with concept drift has become one of hot research spots.However,noise in real data directly affects the result of detection of concept drift and the quality of classification.Therefore,an anti-noise approach is of important value for research and application.Based on the ensemble random decision tree,an effective classification model for stream classification,an incremental approach ICDC was proposed by introducing the Hoeffding Bounds inequality to distinguish concept drift and noise in classification,which adjusts the period of detection and window size for training data in accordance with the detection results.Extensive studies on synthetic and real streaming databases demonstrate that ICDC performs quite effectively compared with several known single or ensemble online algorithms.