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Concept Drift Detection in Data Streams Using Ensemble Classifiers

Dehghan, Mahdie | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42627 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Beigy, Hamid
  7. Abstract:
  8. Concept drift is a challenging problem in the context of data stream processing. As a result of increasing applications of data streams, including network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous studies have been conducted in the field of concept drift detection. In order to solve the problem of concept drift detection, an ideal method should be able to quickly and correctly identify a variety of changes, adapt quickly to new concepts, in the presence of limitations of memory and processing power. In this thesis, a new explicit concept drift detection method based on ensemble classifiers has been proposed for data streams. This method processes samples one by one and monitor the error of the ensemble classification method to detect concept drift. By detection of a drift, a batch classifier will be made on the new concept and will be added to the existing batch classifiers. Also, in this thesis an ensemble learning method for data streams classification is proposed, which uses the proposed concept drift detection method. In this classification method, a new approach to weight and combine the results of classifiers is presented. The proposed method has been evaluated on synthetic and real datasets and has been compared to other methods which exist in this context. It is shown that the method is capable of detecting and adjusting to concept drifts of different speeds and severities. And the advantages of this method in compare to other existing methods in this field is early concept drift detection, increasing the number of correct change detection, reducing the number of incorrect change detection and reduce the loss of accuracy after the concept drift. Specifically, the proposed method could achieve noticeable results in detection of high speed sever concept drifts. The proposed classification method using the proposed detection method, in addition to the above advantages, it’s accuracy is higher than other methods
  9. Keywords:
  10. Data Stream ; Classification ; Ensemble Learning ; Concept Drift ; Online Learning

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