doi: 10.7763/IJMO.2012.V2.169
A New Adaptive Ensemble Boosting Classifier for Concept Drifting Stream Data
Kapil K. Wankhade
1
,
Snehlata S. Dongre
2,3
- 1 Priyadarshini College of Engineering, Nagpur INDIA 440019.
- 2 Sinhgad Institute of Technology, Lonavala, Pune, INDIA
- 3 Department of Computer Science and Engineering, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, INDIA 440019.
Abstract
With the emergence of large-volume and high speed streaming data, mining of stream data has become a focus on increasing interests in real applications including credit card fraud detection, target marketing, network intrusion detection, etc. The major new challenges in stream data mining are, (a) since streaming data may flow in and out indefinitely and in fast speed, it is usually expected that a stream data mining process can only scan a data once and (b) since the characteristics of the data may evolve over time, it is desirable to incorporate evolving features of streaming data. This paper introduced new adaptive ensemble boosting approach for the classification of streaming data with concept drift. This adaptive ensemble boosting method uses adaptive sliding window and Hoeffding Tree with naïve bayes adaptive as base learner. The result shows that the proposed algorithm works well in changing environment as compared with other ensemble classifiers.
Keywords
- Concept drift
- ensemble approach
- hoeffding tree
- sliding window
- stream data
How to Cite
Kapil K. Wankhade and Snehlata S. Dongre, "A New Adaptive Ensemble Boosting Classifier for Concept Drifting Stream Data," International Journal of Modeling and Optimization, vol. 2, no. 4, pp. 493-497, 2012. https://doi.org/ 10.7763/IJMO.2012.V2.169
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).