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.