Multiclass Support Vector Machines for Classification of ECG Data with Missing Values

REFERENCES

• Abe, S. 2010. Support vector machines for pattern classification ( 2nd ed.). London, UK: Springer-Verlag London Limited.
   Google Scholar
• Acuña, E., and C. Rodriguez 2004. The treatment of missing values and its effect in the classifier accuracy. In Classification, clustering and data mining applications, eds. D. Banks et al., 639–48. Berlin, Heidelberg: Springer-Verlag.
   Google Scholar
• Agrafioti, F., J. Gao, and D. Hatzinakos 2011. Heart biometrics: Theory, methods and applications. In Biometrics, ed. J. Yang 199–216. InTech. doi: 10.5772/18113
   Google Scholar
• Anderson, H. S., and M. R. Gupta. 2011. Expected kernel for missing features in support vector machines. In Statistical signal processing workshop (SSP), 2011 IEEE, 285–88. IEEE Conference Publications.
   Google Scholar
• Bache, K., and K. Lichman 2013. UCI machine learning repository: Data sets. Irvine CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml (accessed June 1, 2013).
   Google Scholar
• Boser, B. E., I. M. Guyon, and V. N. Vapnik 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th conference on computational learning theory (CoLT 1992), 144–52. New York, NY: ACM. doi:10.1145/130385.130401.
   Google Scholar
• Chang, C., and C. Lin 2011. LIBSVM: A library for support vector machines. Taipie, Taiwan: National Taiwan University. Initial version 2001 Last updated: May 20.
   Google Scholar
• Chen, P.-H., C.-J. Lin, and B. Schölkopf. 2005. A tutorial on?-Support vector machines. Applied Stochastic Models in Business and Industry 21: 111–36. doi:10.1002/asmb.537.
   Web of Science ®Google Scholar
• Grzymala-Busse, J. W., and M. Hu. 2001. Comparison of several approaches to missing attribute values in data mining. Lecture Notes in Computer Science Volume 2005 378–85. doi: 10.1007/3-540-45554-X_46.
   Google Scholar
• Hamel, L. 2009. Knowledge discovery with support vector machines. Hoboken, NJ: Wiley & Sons.
   Google Scholar
• Hejazi, M., and Y. P. Singh. 2012. Credit data fraud detection using kernel methods with support vector machine. Journal of Advanced Computer Science and Technology Research 2: 35–49.
   Google Scholar
• Hejazi, M., and Y. P. Singh. 2013. One-class support vector machines approach to anomaly detection. Applied Artificial Intelligence: An International Journal 27: 351–66.
   Google Scholar
• Hlalele, N., F. Nelwamondo, and T. Marwala. 2009. Imputation of missing data using PCA, neuro-fuzzy and genetic algorithms. In Advances in neuro-information processing, LNCS 5507:485–92. Berlin, Heidelberg: Springer-Verlag.
   Google Scholar
• Hsu, C.-W., and C.-J. Lin. 2002. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13(2): 415–25. doi: 10.1109/72.991427.
   PubMed Web of Science ®Google Scholar
• Jolliffe, I. T. 2002. Principal component analysis (2nd ed.). New York, NY: Springer.
   Google Scholar
• Karhunen, J. 2011. Robust PCA methods for complete and missing data. Finland: University School of Science, Department of Information and Computer Science.
   Google Scholar
• Kim, Y. S., W. N. Street, and F. Menczer 2002. Feature selection in data mining. In Data mining: Opportunities and challenges, eds. J. Wang, 80–105. Hershey, PA: Idea Group.
   Google Scholar
• Liang, H., S. Lukkarinen, and I. Hartimo. 1997. Heart sound segmentation algorithm based on heart sound envelogram. In Computers in cardiology, 105–108. IEEE. doi: 10.1109/CIC.1997.647841.
   Google Scholar
• Muller, K.-R., S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf. 2001. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2):181–201. doi: 10.1109/72.914517.
   PubMed Web of Science ®Google Scholar
• Pelckmans, K., J. De Brabanter, J. A. K. Suykens, and B. De Moor. 2005. Handling missing values in support vector machine classifiers. Neural Networks 18(5–6):684–92. doi: 10.1016/j.neunet.2005.06.025.
   PubMed Web of Science ®Google Scholar
• Polat, K., B. Akdemir, and S. Güneş. 2008. Computer aided diagnosis of ECG data on the least square support vector machine. Digital Signal Processing 18:25–32. doi: 10.1016/j.dsp.2007.05.006.
   Web of Science ®Google Scholar
• Schölkopf, B., and A. Smola 2002. Learning with kernels support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
   Google Scholar
• Stanimirova, I., M. Daszykowski, and B. Walczak. 2007. Dealing with missing values and outliers in principal component analysis. Talanta 72: 172–78. doi:10.1016/j.talanta.2006.10.011.
   PubMed Web of Science ®Google Scholar
• Tan, P. N., M. Steinbach, and V. Kumar. 2006. Introduction to data mining. Boston, MA: Pearson Addison-Wesley.
   Google Scholar
• Vaerenbergh, S. V. 2009. Kernel methods for nonlinear identification equalization and separation of signals (PhD thesis, Cantabria University).
   Google Scholar
• Van Der Maaten, L. J. P., E. O. Postma, and H. J. Van Den Herik. 2008. Dimensionality reduction: A comparative review. Maastricht, LK: Maastricht University.
   Google Scholar
• Vapnik, V. N. 1995. The nature of statistical learning theory. New York, NY: Springer-Verlag.
   Google Scholar
• Vapnik, V. N. 2000. The nature of statistical learning theory (2nd ed.). New York, NY: Springer-Verlag.
   Google Scholar
• Vapnik, V. N., S. E. Golowich, and A. Smola 1997. Support vector method for function approximation, regression estimation, and signal processing. In Advances in neural information processing systems, vol. 9, eds. M. Mozer, M. Jordan, and T. Petsche, 281–87. Cambridge, MA: MIT Press. doi:10.1.1.41.3139.
   Google Scholar
• Weston, J., and C. Watkins 1998. Multi-class support vector machines. In Proceedings of the 6th European symposium on artificial neural networks, 259–66. Bruges, Belgium: ESANN. doi:10.1.1.50.9594.
   Google Scholar
• Zhu, X., S. Zhang, Z. Jin, Z. Zhang, and Z. Xu. 2011. Missing value estimation for mixed-attribute data sets. IEEE Transactions on Knowledge and Data Engineering 23(1):110–21. doi: 10.1109/TKDE.2010.99.
   Web of Science ®Google Scholar