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Computational Intelligence and Neuroscience
Volume 2015, Article ID 423581, 8 pages
http://dx.doi.org/10.1155/2015/423581
Research Article

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

1School of Computer Software, Tianjin University, Tianjin 300072, China
2Centre for Excellence in Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK
3School of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Received 24 March 2015; Revised 8 May 2015; Accepted 11 May 2015

Academic Editor: Pietro Aricò

Copyright © 2015 Yi Zhang 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.

Linked References

  1. W. Wei and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Transactions on Communications, vol. 48, no. 2, pp. 189–193, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Liu, W. Shi, and H. Zhang, “A fuzzy topology-based maximum likelihood classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 1, pp. 103–114, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. H.-L. Huang, C.-C. Lee, and S.-Y. Ho, “Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers,” BioSystems, vol. 90, no. 1, pp. 78–86, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. X. He and Y. Zhao, “Prior knowledge guided maximum expected likelihood based model selection and adaptation for nonnative speech recognition,” Computer Speech and Language, vol. 21, no. 2, pp. 247–265, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Hall and S. Minyaev, “Chemical analyses of Xiong-nu pottery: a preliminary study of exchange and trade on the inner Asian steepes,” Journal of Archaeological Science, vol. 29, no. 2, pp. 135–144, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. Q. Y. Hong and S. Kwong, “A genetic classification method for speaker recognition,” Engineering Applications of Artificial Intelligence, vol. 18, no. 1, pp. 13–19, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Goel and M. Pal, “Application of support vector machines in scour prediction on grade-control structures,” Engineering Applications of Artificial Intelligence, vol. 22, no. 2, pp. 216–223, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Yélamos, G. Escudero, M. Graells, and L. Puigjaner, “Performance assessment of a novel fault diagnosis system based on support vector machines,” Computers and Chemical Engineering, vol. 33, no. 1, pp. 244–255, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Subasi and M. I. Gursoy, “EEG signal classification using PCA, ICA, LDA and support vector machines,” Expert Systems with Applications, vol. 37, no. 12, pp. 8659–8666, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. B. C. Ko, K.-H. Cheong, and J.-Y. Nam, “Fire detection based on vision sensor and support vector machines,” Fire Safety Journal, vol. 44, no. 3, pp. 322–329, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Maldonado-Bascón, S. Lafuente-Arroyo, P. Gil-Jiménez, H. Gómez-Moreno, and F. López-Ferreras, “Road-sign detection and recognition based on support vector machines,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 264–278, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Pal and P. M. Mather, “Assessment of the effectiveness of support vector machines for hyperspectral data,” Future Generation Computer Systems, vol. 20, no. 7, pp. 1215–1225, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Huang, L. S. Davis, and J. R. G. Townshend, “An assessment of support vector machines for land cover classification,” International Journal of Remote Sensing, vol. 23, no. 4, pp. 725–749, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Waske and J. A. Benediktsson, “Fusion of support vector machines for classification of multisensor data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 3858–3866, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. B. W. Szuster, Q. Chen, and M. Borger, “A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones,” Applied Geography, vol. 31, no. 2, pp. 525–532, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Ren, “ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging,” Knowledge-Based Systems, vol. 26, no. 2, pp. 144–153, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. C.-M. Vong, P.-K. Wong, and Y.-P. Li, “Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference,” Engineering Applications of Artificial Intelligence, vol. 19, no. 3, pp. 277–287, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Vega, A. Murari, G. Vagliasindi, and G. A. Ratt, “Automated estimation of L/H transition times at JET by combining Bayesian statistics and support vector machines,” Nuclear Fusion, vol. 49, no. 8, Article ID 085023, 11 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. C. C. Hsu, K. S. Wang, and S. H. Chang, “Bayesian decision theory for support vector machines: imbalance measurement and feature optimization,” Expert Systems with Applications, vol. 38, no. 5, pp. 4698–4704, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. G. M. Foody, “RVM-based multi-class classification of remotely sensed data,” International Journal of Remote Sensing, vol. 29, no. 6, pp. 1817–1823, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Lee, Y. Lin, and G. Wahba, “Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data,” Journal of the American Statistical Association, vol. 99, no. 465, pp. 67–81, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  24. K. Crammer and Y. Singer, “On the algorithmic implementation of multiclass Kernel-based vector machines,” Journal of Machine Learning Research, vol. 2, pp. 265–292, 2001. View at Google Scholar
  25. J. Platt, “Probabilistic outputs for support Vector machines and comparisons to regularized likelihood methods,” in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, Eds., MIT, Cambridge, Mass, USA, 2000. View at Google Scholar
  26. H. T. Lin, C. J. Lin, and R. C. Weng, “A note on Platt's probabilistic outputs for support vector machines,” Machine Learning, vol. 68, no. 3, pp. 267–276, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. V. Franc, A. Zien, and B. Scholkopf, “Support vector machines as probabilistic models,” in Proceedings of the 28th International Conference on Machine Learning (ICML '11), Bellevue, Wash, USA, June-July 2011.
  28. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, pp. 1–27, 2011, http://www.csie.ntu.edu.tw/~cjlin/libsvm. View at Google Scholar
  29. A. Frank and A. Asuncion, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html.
  30. D. P. Doane and L. E. Seward, “Measuring skewness: a forgotten statistic?” Journal of Statistics Education, vol. 19, no. 2, pp. 1–18, 2011. View at Google Scholar · View at Scopus
  31. J. Jiang, P. Trundle, and J. Ren, “Medical image analysis with artificial neural networks,” Computerized Medical Imaging and Graphics, vol. 34, no. 8, pp. 617–631, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Ren, “ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging,” Knowledge-Based Systems, vol. 26, pp. 144–153, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Ren, D. Wang, and J. Jiang, “Effective recognition of MCCs in mammograms using an improved neural classifier,” Engineering Applications of Artificial Intelligence, vol. 24, no. 4, pp. 638–645, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. C. Zhao, X. Li, J. Ren, and S. Marshall, “Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery,” International Journal of Remote Sensing, vol. 34, no. 24, pp. 8669–8684, 2013. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Kelman, J. Ren, and S. Marshall, “Effective classification of Chinese tea samples in hyperspectral imaging,” Artificial Intelligence Research, vol. 2, no. 4, 2013. View at Publisher · View at Google Scholar