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The Scientific World Journal
Volume 2014, Article ID 243921, 8 pages
Research Article

Analyzing Big Data with the Hybrid Interval Regression Methods

1Department of Business Administration, National Taipei University of Business, No. 321, Section 1, Jinan Road, Zhongzheng District, Taipei City 100, Taiwan
2Department of Information Management, Hwa Hsia Institute of Technology, No. 111, Gongzhuan Road, Zhonghe District, New Taipei City 235, Taiwan
3Department of Computer Science and Information Engineering, National Dong Hwa University, No. 123, Hua-Shi Road, Hualien 97063, Taiwan

Received 19 May 2014; Accepted 7 July 2014; Published 20 July 2014

Academic Editor: Jung-Fa Tsai

Copyright © 2014 Chia-Hui Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.