Parallel Predicting Algorithm Based on Support Vector Regression Machine

Author

Jia, Ronggang ; Lei, Yongmei ; Chen, Gaozhao ; Fan, Xuening

Author_Institution

Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China

fYear

2012

fDate

May 30 2012-June 1 2012

Firstpage

488

Lastpage

493

Abstract

Using support vector regression machine to predict a large-scale dataset, which will take a long time. In order to solve the problem, this paper proposes a parallel predicting algorithm based on sample separation, and introduces the design and implementation of the algorithm. The performance of the algorithm has been evaluated and analyzed with KDD99 dataset on the ZQ3000 cluster. Experimental results show that the algorithm not only effectively reduces the time of predicting dataset, but also keeps high accuracy rate.

Keywords

data handling; parallel algorithms; regression analysis; software performance evaluation; support vector machines; KDD99 dataset; ZQ3000 cluster; algorithm performance evaluation; large-scale dataset prediction; parallel predicting algorithm; sample separation; support vector regression machine; Algorithm design and analysis; Educational institutions; Kernel; Prediction algorithms; Predictive models; Support vector machines; Training; KDD99 dataset; master-slave mode; parallel predicting; support vector regression machine (SVR);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4673-1536-4

Type

conf

DOI

10.1109/ICIS.2012.82

Filename

6211143