Abstract
High-speed data stream is a data flow velocity exceeds the processing power of integrated classifier; integrated classifier training can not reach all the most recent data to update the classification model. To this end, this chapter introduces the optimal Bayesian classification theory, and its integration on the basis of analysis of the expected classification error of the bias variance decomposition, and finally presents a sampling bias based on an integrated high-speed data stream classification algorithm (Ensemble Classifiers Algorithm for Classify High Speed Data Stream based of Biased Sample, CDSBS), theoretical analysis is the experimental verification show that the algorithm can effectively reduce the integrated classifier training update at the same time, the classification remains a high classification performance.
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JianPing, G. (2012). The Research on Model of Group Behavior Based on Mobile Network Mining and High-Speed Data Streams. In: Mao, E., Xu, L., Tian, W. (eds) Emerging Computation and Information teChnologies for Education. Advances in Intelligent and Soft Computing, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28466-3_64
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DOI: https://doi.org/10.1007/978-3-642-28466-3_64
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