An Improved Parallel Bayesian Text Classification Algorithm

An Improved Parallel Bayesian Text Classification Algorithm

Panpan Shen Hao Wang  Zhouqing Meng  Zhenyu Yang  Zhaoping Zhi  Ran Jin  Aimin Yang 

School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, China

Corresponding Author Email: 
1172340155@qq. com
Page: 
6-10
|
DOI: 
10.18280/rces.030102
Received: 
|
Accepted: 
|
Published: 
31 March 2016
| Citation

OPEN ACCESS

Abstract: 

Used the idea of cloud computing, according to MapReduce model to solve the traditional Bayesian classification algorithm suited to large-scale data deficiencies, greatly improved the speed of classification. The combination of the characteristics of the parallel algorithm was improved accordingly. Adding synonyms and word frequency filtering combined approach allows vector dimensionality reduction, reducing false positives. Wherein the particular keyword was then weighted to enhance the accuracy of the classification. Finally, the Hadoop cloud computing platform was experimentally proved that the traditional text classification algorithm after parallelization on Hadoop cloud computing platforms, has better speedup, and the improved algorithm can improve the classification accuracy.

Keywords: 

Cloud computing, Text classification, Parallel, Hadoop

1. Introduction
2. Naive Bias Classification Algorithm and Its Paralleization
3. Classification Algorithms in Cloud Computing Environment
4. Experimental Results and Analysis on Cloud Platform
5. Conclusions
Acknowledgement
  References

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