Classification Algorithm for Data Stream Based on Mixture Models of C4.5 and NB
Xuegang Hu · 2010
Classification on the noisy data stream with concept drifts has recently become one of the most popular topics in streaming data mining.Classification algorithm for mining Data Streams based on Mixture Models of C4.5 and NB was proposed,called CDSMM,in which decision trees based on C4.5 are selected as the basic classifiers and the classifier of Nave Bayes is adopted to filter noise data.Meanwhile,it introduces the μ-hypothesis testing method to detect concept drifts.Extensive studies demonstrate that CDSMM is superior to several existing algorithms in the predictive accuracy when handling noisy data streams with concept drifts.