Extended Dynamic Weighted Majority Using Diversity to Handle Drifts

  • Parneeta SidhuEmail author
  • M. P. S Bhatia
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 241)


Concept drift is the recent trend of online data. The distribution underlying the data is changing with time .There are many algorithms developed in the literature to handle such drifting data concepts. In our paper we are outlining the framework of our new approach to handle drifts which will be based on the concept of diversity. Diversity is the measure of variation in the predictive accuracy of ensemble members. Our approach would implement diversity concept first time on the online approach that does not explicitly use a mechanism to handle drifts. This type of online approach would give better accuracy at a slight increase in the running time and memory. In our paper we would also outline the main objectives behind our research and the state of the art in data stream mining.


Data Streams Concept Drift Diversity Ensemble Techniques 


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyUniversity of Delhi, DwarkaNew DelhiIndia

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