CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

by Abdiansah Abdiansah, Retantyo Wardoyo
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 3
Year of Publication: 2015
Authors: Abdiansah Abdiansah, Retantyo Wardoyo
10.5120/ijca2015906480

Abdiansah Abdiansah, Retantyo Wardoyo . Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM. International Journal of Computer Applications. 128, 3 ( October 2015), 28-34. DOI=10.5120/ijca2015906480

@article{ 10.5120/ijca2015906480,
author = { Abdiansah Abdiansah, Retantyo Wardoyo },
title = { Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 3 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number3/22854-2015906480/ },
doi = { 10.5120/ijca2015906480 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:20:18.716169+05:30
%A Abdiansah Abdiansah
%A Retantyo Wardoyo
%T Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 3
%P 28-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Support Vector Machines (SVM) is one of machine learning methods that can be used to perform classification task. Many researchers using SVM library to accelerate their research development. Using such a library will save their time and avoid to write codes from scratch. LibSVM is one of SVM library that has been widely used by researchers to solve their problems. The library also integrated to WEKA, one of popular Data Mining tools. This article contain results of our work related to complexity analysis of Support Vector Machines. Our work has focus on SVM algorithm and its implementation in LibSVM. We also using two popular programming languages i.e C++ and Java with three different dataset to test our analysis and experiment. The results of our research has proved that the complexity of SVM (LibSVM) is O(n3) and the time complexity shown that C++ faster than Java, both in training and testing, beside that the data growth will be affect and increase the time of computation.

References
  1. Agarwal. S (2011). Weighted support vector regression approach for remote healthcare monitoring. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, pp. 969–974.Che, JinXing. (2013). Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm, Appl. Soft Comput. 13 (8), pp. 3473–3481.
  2. Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
  3. Gunn S, R . (1998). Support vector machines for classification and regression, ISIS technical report 14.
  4. Hofmann, T., Schölkopf, B., & Smola, A. J. (2008). Kernel methods in machine learning. The annals of statistics, 1171-1220.
  5. Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on, 13(2), 415-425.
  6. Huang, Weimin, Leping Shen. (2008). Weighted support vector regression algorithm based on data description. In ISECS International Colloquium on Computing, Communication, Control, and Management CCCM’08, vol. 1, IEEE, pp. 250–254.
  7. Lee, Y, Lin, Y, G. Wahba. (2001). Multicategory support vector machines. Comput. Sci. Stat. 33, pp. 498–512.
  8. Lin, C. J., Hsu, C. W., & Chang, C. C. (2003 – Last updated: April 15, 2010). A practical guide to support vector classification. National Taiwan U., www. csie. ntu. edu. tw/cjlin/papers/guide/guide. Pdf.
  9. Nemmour, H, Chibani, Y. (2006). Multi-class SVMs based on fuzzy integral mixture for handwritten digit recognition. Geometric modeling and imaging—new trends, pp. 145–149.
  10. Suykens, A.K. Johan, Brabanter Jos De, Lukas Lukas, Vandewalle Joos. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48 (1) 85–105.
  11. Tomar, Divya, Arya Ruchi, Agarwal Sonali. (2011). Prediction of profitability of industries using weighted SVR. Int. J. Comput. Sci. Eng. 3 (5) pp. 1938–1945.
  12. Tsang, I. W., Kwok, J. T., & Cheung, P. M. (2005). Core vector machines: Fast SVM training on very large data sets. In Journal of Machine Learning Research (pp. 363-392).
  13. Vapnik, V. (2000). The nature of statistical learning theory. Springer Science & Business Media.
  14. Webb, A. R. (2002). Statistical pattern recognition, 2nd Edition. John Wiley & Sons.
  15. Weston, J, Watkins, C. (1998). Multi-class support vector machines. CSD-TR-98-04 royal holloway, University of London, Egham, UK.
  16. Xue, Zhenxia, Liu Wanli. (2012). A fuzzy rough support vector regression machine. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Dover, pp. 840–844.
  17. Zhang, D., & Lee, W. S. (2003). Question classification using support vector machines. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (pp. 26-32). ACM.
  18. Zhu, G., Huang, D., Zhang, P., & Ban, W. (2015). ε-Proximal support vector machine for binary classification and its application in vehicle recognition. Neurocomputing, 161, 260-266.
Index Terms

Computer Science
Information Sciences

Keywords

SVM LibSVM C++ Java WEKA Data Mining

Powered by PhDFocusTM