论文信息 - Algorithm to handle Concept Drifting Concept Drifting Concept Drifting Concept Drifting in in Stream Data Stream Data Stream Data Stream Mining Mining

Algorithm to handle Concept Drifting Concept Drifting Concept Drifting Concept Drifting in in Stream Data Stream Data Stream Data Stream Mining Mining

Data Stream Mining is the evolving field of research. Mining continuous data streams brings unique opportunities but also new challenges. This paper will describe and evaluate the proposed classifier which uses ensemble classifier along with the boosting concept. Adaptive windowing is also used for handling the data stream. Empirical study will show that the proposed classifier takes less memory, less time, gives the good accuracy also handles the concept drift.

G. H. Raisoni | Latesh Malik

[1] Bhavani M. Thuraisingham,et al. A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams , 2009, PAKDD.

[2] Geoff Hulten,et al. Mining time-changing data streams , 2001, KDD '01.

[3] Geoff Holmes,et al. New ensemble methods for evolving data streams , 2009, KDD.

[4] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.

[5] Yang Zhang,et al. Decision Tree for Dynamic and Uncertain Data Streams , 2010, ACML.

[6] Stuart J. Russell,et al. Experimental comparisons of online and batch versions of bagging and boosting , 2001, KDD '01.

[7] Geoff Hulten,et al. Mining high-speed data streams , 2000, KDD '00.

[8] Marcus A. Maloof,et al. Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.

[9] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.

[10] Marcus A. Maloof,et al. Using additive expert ensembles to cope with concept drift , 2005, ICML.

[11] Stuart J. Russell,et al. Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.