Fast Support Vector Machine Classification for Large Data Sets

International Journal of Computational Intelligence Systems

Volumn 7, Issue 2, 2014, Pages 197-212

  Li, Xiaoou ^a   Yu, Wen ^a  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

Author keywords

classification; large data sets; support vector machine

Indexed keywords

BIOINFORMATICS; CLASSIFICATION (OF INFORMATION);

BENCH-MARK PROBLEMS; CLASSIFICATION ACCURACY; CLUSTERING TECHNIQUES; FAST SUPPORT VECTOR MACHINES; LARGE DATASETS; SUPPORT VECTOR MACHINE ALGORITHM; SVM CLASSIFICATION; TRAINING COMPLEXITY;

SUPPORT VECTOR MACHINES;

EID: 84900010973     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2013.868148     Document Type: Article

References (39)

1. M. Awad L. Khan, F. Bastani and I. L. Yen, An Effective support vector machine(SVMs) Performance Using Hierarchical Clustering, Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), Pages: 663-667, 2004
2. G. Babu and M. Murty, A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm, Pattern Recognit. Lett., vol. 14, no. 10, pp. 763-769, 1993.
3. M. Badoiu, S. Har-Peled and P. Indyk. Approximate clustering via core-sets. Proceedings of the 34th Symposium on Theory of Computing, 2002.
4. P. Burman, A Comparative Study of Ordinary Cross- Validation, v-Fold Cross-Validation and the Repeated Learning-Testing Methods, Biometrika, Vol. 76, No. 3, pp. 503-514, 1989
5. J. Cervantes, X. Li and W. Yu, Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets, MICAI 2006: Advances in Artificial Intelligence, Srpinger-Verlgag, Lecture Notes in Computer Science (LNCS), vol. 4293, 572-582, 2006
6. C-C. Chang and C-J. Lin, LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
7. Chen J.-H. and Chen Ch.-S., Reducing SVM classification time using multiple mirror classifiers, Systems, Man, and Cybernetics, Part B, IEEE Transactions on, April 2004, Vol: 34, Issue: 2, pp: 1173-1183
8. P-H. Chen, R-E. Fan and C-J. Lin, A Study on SMO-Type Decomposition Methods for Support Vector Machines, IEEE Trans. Neural Networks, Vol.17, No.4, 893-908, 2006.
9. R. Collobert and S. Bengio, SVMTorch: Support vector machines for large regression problems, Journal of Machine Learning Research, Vol.1, 143-160, 2001.
10. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
11. J-X. Dong, A. Krzyzak, and C.Y. Suen, Fast SVM Training Algorithm with Decomposition on Very Large Data Sets, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.4, pp.603-618, 2005
12. G. Folino,C. Pizzuti and G. Spezzano, GP Ensembles for Large-Scale Data Classification, IEEE Trans. Evol. Comput., vol.10, no.5, pp.604-616, 2006.
13. B.V. Gnedenko, Y.K. Belyayev, and A.D. Solovyev, Mathematical Methods of Reliability Theory, NewYork: Academic Press, 1969
14. M. Girolami, Mercer kernel based clustering in feature space, IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 780-784, May 2002.
15. T. Hastie and R. Tibshirani, Discriminant adaptive nearest neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 18, no 6, 607-616, 1996
16. G.B. Huang, K.Z. Mao, C.K. Siew and D.-S. Huang, Fast Modular Network Implementation for Support Vector Machines, IEEE Trans. on Neural Networks, 2006
17. T. Joachims, Making large-scale support vector machine learning practice, Advances in Kernel Methods: Support Vector Machine. MIT Press, Cambridge, MA 1998.
18. Khan L., Awad M and Thuraisingham B., A new intrusion detection system using support vector machines and hierarchical clustering, The VLDB Journal, Vol. 16, No. 4, pp 507-521, 2007.
19. S-W. Kim and B.J. Oommen, Enhancing Prototype Reduction Schemes with Recursion: A Method Applicable for "Large" Data Sets, IEEE Trans. Syst., Man, Cybern. B, Vol.34, No.3, 1184-1397, 2004.
20. P. Kumar, J. S. B. Mitchell, and A. Yildirim. Approximate minimum enclosing balls in high dimensions using core-sets. ACM Journal of Experimental Algorithmics, 8 January 2003.
21. C-T. Lin, C-M. Yeh, S-F. Liang, J-F. Chung and N. Kumar, Support-Vector-Based Fuzzy Neural Network for Pattern Classification, IEEE Trans. Fuzzy Syst., Vol.14, No.1, 31-41, 2006.
22. M.E. Mavroforakis and S. Theodoridis, A Geometric Approach to Support Vector Machine(SVM) Classification, IEEE Trans. Neural Networks, Vol.17, No.3, 671-682, 2006.
23. O. L. Mangasarian and D. R. Musicant, Successive overrelaxation for support vector machines, IEEE Trans. on Neural Networks, vol.10, pp.1032-1037, 1999.
24. W.S. Noble, S. Kuehn, R. Thurman, M. Yu and J. Stamatoyannopoulos, Predicting the in vivo signature of human gene regulatory sequences, by. Bioinformatics, Vol.21, No.1, i338-343, 2005.
25. A. Nurnberger, W. Pedrycz, R. Kruse, Data mining tasks and methods: Classification: neural network approaches, Oxford University Press, Inc., New York, NY, 2002
26. N. Pal and J. Bezdek, On cluster validity for the fuzzy c-means model, IEEE Trans. Fuzzy Syst., vol. 3, no. 3, pp. 370-379, Aug. 1995.
27. J. Platt, Fast Training of support vector machine using sequential minimal optimization, Advances in Kernel Methods: support vector machine, MIT Press, Cambridge, MA, 1998.
28. C. Pizzuti and D. Talia, P-Auto Class: Scalable Parallel Clustering for Mining Large Data Sets, IEEE Trans. Knowledge and Data Eng., vol.15, no.3, pp.629-641, 2003.
29. D. Prokhorov. IJCNN 2001 neural network competition, Ford Research Laboratory, http://www.geocities.com/ijcnn/nnc_ijcnn01.pdf, 2001.
30. R Safavian, D Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, no 3, pp. 660-674, 1991.
31. Saketha Nath J., Bhattacharyya C., Murty M. N., Clustering based large margin classification: A scalable approach using SOCP formulation, Proceedings of the Twelfth ACM SIGKDD international conference on knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
32. G. Schohn and D. Cohn, Less is more: Active Learning with support vector machines, Proc. 17th Int. Conf. Machine Learning, Stanford, CA, 2000.
33. L. Shih, D.M. Rennie, Y. Chang and D.R. Karger, Text Bundling: Statistics-based Data Reduction, Proc of the Twentieth Int. Conf. on Machine Learning (ICML-2003), Washington DC, 2003.
34. P. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Pearson Addison Wesley, May, 2005
35. V. Tresp, A Bayesian Committee Machine, Neural Computation, vol.12, no.11, pp.2719-2741, 2000.
36. Uzilov AV, Keegan JM, Mathews DH, Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change, BMC Bioinformatics, Vol.173, No.7, 2006
37. Y.S. Xia and J. Wang, A One-layer Recurrent Neural Network for Support Vector Machine Learning, IEEE Transactions on Systems, Man and Cybernetics - Part B, pp. 1261-1269, 2004.
38. R. Xu and D. WunschII, Survey of Clustering Algorithms, IEEE Trans. Neural Networks, Vol.16, No.3, 645-678, 2005.
39. H. Yu, J. Yang and J. Han, Classifying Large Data Sets Using SVMs with Hierarchical Clusters, Proc. of the 9th ACM SIGKDD 2003, Washington, DC, USA, 2003.