The Research of Data Stream Classification Based on Rough Set Theory-Neural Network Integration

Zhi Bo Ren; Chun Miao Yan; Yu Zhou Wei; Lei Sun

doi:10.4028/www.scientific.net/AMM.441.717

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 441The Research of Data Stream Classification Based...

The Research of Data Stream Classification Based on Rough Set Theory-Neural Network Integration

Abstract:

According to the high speed of data arriving, a large amount of data and concept drifting in the stream model, combining the techniques of rough set theory, neural network and voting rule, we put forward a new data stream classification model, which is a multi-classifier integration based on rough set theory, neural network. Firstly, it reduces all attributes using rough set theory; secondly, it constructs base classifiers on the data chunks after the reduction of attributes using the improved BP neural network; finally, it fuses various base classifiers into an ensemble by voting rule. Through applying the model to classify data stream, the experiment results show that the ensemble method is feasible and effective.

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Periodical:

Applied Mechanics and Materials (Volume 441)

Pages:

717-720

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.441.717

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Online since:

December 2013

Authors:

Zhi Bo Ren, Chun Miao Yan, Yu Zhou Wei, Lei Sun

Keywords:

BP Neural Network (BPNN), Data Stream, Ensemble Learning, Rough Set Theory (RST), Voting Rule

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