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

被引：1
|
作者：
Ren, Zhibo [1 ]
Yan, Chunmiao [1 ]
Wei, Yuzhou [2 ]
Sun, Lei [1 ]
机构：
[1] Hebei Univ, Baoding 071000, Peoples R China
[2] North China Elect Power Univ, Baoding 071000, Peoples R China
来源：
MACHINERY ELECTRONICS AND CONTROL ENGINEERING III | 2014年 / 441卷
关键词：
Data stream; Rough set theory; BP neural network; Voting rule; ensemble learning; CONCEPT DRIFT; CLASSIFIERS;
D O I：
10.4028/www.scientific.net/AMM.441.717
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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; fmally, 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.
引用
收藏
页码：717 / +
页数：2
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