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Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation Cover

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Open Access
|Jan 2019

References

  1. Bala, M. and Agrawal, R.K. (2011). Optimal decision tree based multi-class support vector machine, Informatica 35(2): 197-209.
  2. Bartlett, P.L. and Shawe-Taylor, J. (1999). Generalization performance of support vector machines and other pattern classifiers, in B. Schölkopf et al. (Eds.), Advances in Kernel Methods, MIT Press, Cambridge, MA, pp. 43-54.10.7551/mitpress/1130.003.0007
  3. Blake, C.L. and Merz, C.J. (1998). UCI Repository of Machine Learning Databases, University of California, Irvine, CA, http://archive.ics.uci.edu/ml/.
  4. Bredensteiner, E.J. and Bennett, K.P. (1999). Multicategory classification by support vector machines, Computational Optimization 12(1-3): 53-79.10.1023/A:1008663629662
  5. Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2(2): 121-167.10.1023/A:1009715923555
  6. Chen, J., Wang, C. and Wang, R. (2009). Adaptive binary tree for fast SVM multiclass classification, Neurocomputing 72(13-15): 3370-3375.10.1016/j.neucom.2009.03.013
  7. Cheong, S., Hoon Oh, S. and Lee, S.-Y. (2004). Support vector machines with binary tree architecture for multi-class classification, Neural Information Processing Letters 2(3): 47-51.
  8. Chmielnicki, W. and Sta˛por, K. (2016). Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification, International Journal of Applied Mathematics and Computer Science 26(1): 191-201, DOI: 10.1515/amcs-2016-0013.10.1515/amcs-2016-0013
  9. Crammer, K. and Singer, Y. (2002). On the learnability and design of output codes for multiclass problems, Machine Learning 47(2-3): 201-233.10.1023/A:1013637720281
  10. Dong, C., Zhou, B. and Hu, J. (2015). A hierarchical SVMbased multiclass classification by using similarity clustering, International Joint Conference on Neural Networks, Killarney, Ireland, pp.1-6.10.1109/IJCNN.2015.7280489
  11. Fei, B. and Liu, J. (2006). Binary tree of SVM: A new fast multiclass training and classification algorithm, IEEE Transactions on Neural Networks 17(3): 696-704.10.1109/TNN.2006.87234316722173
  12. Friedman, J. (1996). Another approach to polychotomous classification, Technical report, Stanford University, Stanford, CA.
  13. García, S., Fernández, A., Luengo, J. and Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences 180(10): 2044-2064.10.1016/j.ins.2009.12.010
  14. Hastie, T. and Tibshirani, R. (1998). Classification by pairwise coupling, Annals of Statistics 26(2): 451-471.10.1214/aos/1028144844
  15. Hsu, C. and Lin, C. (2002). A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks 13(2): 415-425.10.1109/72.99142718244442
  16. Joachims, T. (1999). Making large-scale SVM learning practical, in B. Schölkopf et al. (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA.
  17. Kijsirikul, B., Ussivakulz, N. and Road, P. (2002). Multiclass support vector machines using adaptive directed acyclic graph, International Joint Conference on Neural Networks, Honolulu, HI, USA, pp. 980-985.
  18. Knerr, S., Personnaz, L. and Dreyfus, G. (1990). Single-layer learning revisited: A stepwise procedure for building and training a neural network, Neurocomputing 68(68): 41-50.10.1007/978-3-642-76153-9_5
  19. Kumar, M.A. and Gopal, M. (2010). Fast multiclass SVM classification using decision tree based one-against-all method, Neural Processing Letters 32(3): 311-323.10.1007/s11063-010-9160-y
  20. Kumar, M.A. and Gopal, M. (2011). Reduced one-against-all method for multiclass svm classification, Expert Systems with Applications 38(11): 14238-14248.
  21. Lei, H. and Govindaraju, V. (2005). Half-against-halfmulti-class support vector machines, in N.C. Oza et al. (Eds.), Multiple Classifier Systems, MCS 2005, Lecture Notes in Computer Science, Vol. 3541, Springer, Berlin/Heidelberg, pp. 156-164.10.1007/11494683_16
  22. Liu, B., Cao, L., Yu, P.S. and Zhang, C. (2008). Multi-space-mapped SVMs for multi-class classification, Proceedings of 8th IEEE International Conference on Data Mining, Washington, DC, USA, Vol. 8, pp. 911-916.10.1109/ICDM.2008.13
  23. Madzarov, G., Gjorgjevikj, D. and Chorbev, I. (2009). A multi-class SVM classifier utilizing binary decision tree support vector machines for pattern recognition, Electrical Engineering 33(1): 233-241.
  24. Platt, J., Cristianini, N. and Shawe-Taylor, J. (2000). Large margin DAGs for multiclass classification, in S.A. Solla et al. (Eds.), Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, pp. 547-553.
  25. Songsiri, P., Kijsirikul, B. and Phetkaew, T. (2008). Information-based dicrotomizer: A method for multiclass support vector machines, IEEE International Joint Conference on Neural Networks, Hong Kong, China, pp. 3284-3291.10.1109/IJCNN.2008.4634264
  26. Songsiri, P., Phetkaew, T. and Kijsirikul, B. (2015). Enhancement of multi-class support vector machine construction from binary learners using generalization performance, Neurocomputing 151(P1): 434-448.10.1016/j.neucom.2014.09.021
  27. Takahashi, F. and Abe, S. (2002). Decision-tree-based multiclass support vector machines, Proceedings of the 9th International Conference on Neural Information Processing, ICONIP’02, Singapore, Singapore, Vol. 3, pp. 1418-1488.
  28. Vapnik, V.N. (1998). Statistical Learning Theory, John Wiley & Sons, New York, NY.
  29. Vapnik, V.N. (1999). An overview of statistical learning theory, IEEE Transactions on Neural Networks 10(5): 988-99. Vapnik V.N., C.A. (1974). Teoriya Raspoznavaniya Obrazov: Statisticheskie Problemy Obucheniya (Theory of Pattern Recognition: Statistical Problems of Learning), Nauka, Moscow.10.1109/72.788640
  30. Yang, X., Yu, Q., He, L. and Guo, T. (2013). The one-against-all partition based binary tree support vector machine algorithms for multi-class classification, Neurocomputing 113(3): 1-7.10.1016/j.neucom.2012.12.048
DOI: https://doi.org/10.2478/amcs-2018-0054 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 705 - 717
Submitted on: Oct 3, 2017
Accepted on: May 18, 2018
Published on: Jan 11, 2019
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Pittipol Kantavat, Boonserm Kijsirikul, Patoomsiri Songsiri, Ken-Ichi Fukui, Masayuki Numao, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.