Title :
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
fDate :
May 30 2012-June 1 2012
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);
Conference_Titel :
Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4673-1536-4
DOI :
10.1109/ICIS.2012.82
