584
Views
10
CrossRef citations to date
0
Altmetric
Original Articles

Algorithms Analysis in Adjusting the SVM Parameters: An Approach in the Prediction of Protein Function

, &

References

  • Ben-Hur, A., and J. Weston. 2010. A User’s guide to support vector machines. In Data mining techniques for the life sciences, Humana Press, Totowa, NJ, 223–39. Springer.
  • Berman, H. M., J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. 2000. The protein data bank. Nucleic Acids Research 28(1):235–42. doi:10.1093/nar/28.1.235.
  • Boser, B. E., I. M. Guyon, and V. N. Vapnik. 1992. A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual Workshop on Computational Learning Theory, (COLT’92), 144–52. New York, NY: ACM.
  • Chang, -C.-C., and C.-J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27:1–27:27.
  • Conforti, D., and R. Guido. 2010. Kernel based support vector machine via semidefinite programming: Application to medical diagnosis. Computers and Operations Research 37 (8):1389–94. Operations Research and Data Mining in Biological Systems.
  • Dobson, P. D., and A. J. Doig. 2005. Predicting enzyme class from protein structure without alignments. Journal of Molecular Biology 345 (1):187–99.
  • Elmezain, M., A. Al-Hamadi, O. Rashid, and B. Michaelis. 2009. Posture and gesture recognition for human-computer interaction. In-tech, Advanced Technologies, Kankesu Jayanthakumaran (Ed.), InTech, doi: 10.5772/8221. Available from: https://www.intechopen.com/books/advanced-technologies/posture-and-gesture-recognition-for-human-computer-interaction.
  • Guo, G., S. Z. Li, and K. L. Chan. 2000. Face recognition by support vector machines. Proceedings of 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 196–201. doi: 10.1109/AFGR.2000.840634
  • Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. 2009. The weka data mining software: An update. ACM SIGKDD Explorations Newsletter 11 (1):10–18.
  • Hassan, R., B. Cohanim, and O. de Weck. 2005. Comparison of particle swarm optimization and the genetic algorithm. Proceedings of 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, number AIAA-2005-1897. Austin, TX: American Institute of Aeronautics and Astronautics. doi: http://dx.doi.org/10.2514/6.2005-1897.
  • Herbrich, R. 2001. Learning kernel classifiers: Theory and Algorithms. Cambridge, MA, USA: MIT Press.
  • Huang, C.-L., and C.-J. Wang. 2006. A ga-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31 (2):231–40.
  • İlhan, İ., and G. Tezel. 2013. A genetic algorithm–support vector machine method with parameter optimization for selecting the tag snps. Journal of Biomedical Informatics 46 (2):328–40.
  • Joachims, T. 2002. Learning to classify text using support vector machines: Methods, theory and algorithms. Norwell, MA, USA: Kluwer Academic Publishers.
  • Kennedy, J., and R. C. Eberhart. 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, vol. 4, Perth, WA, Austrália, 1942–48. doi: 10.1109/ICNN.1995.488968.
  • Kianmehr, K., and R. Alhajj. 2008. Effectiveness of support vector machine for crime hot-spots prediction. Applied Artificial Intelligence 22 (5):433–58.
  • Leijôto, L. F., T. A. O. Rodrigues, L. E. Zárate, and C. N. Nobre. 2014. A Genetic Algorithm for the Selection of Features Used in the Prediction of Protein Function. IEEE 14th International Conference on Bioinformatics and Bioengineering, Boca Raton, FL, USA, 168–74. doi: 10.1109/BIBE.2014.42.
  • Nelson, D. L., and M. M. Cox. 2017. Lehninger Principles of Biochemistry. 7a edition. WH Freeman and Company.
  • Neshich, G., W. Rocchia, A. L. Mancini, M. E. Yamagishi, P. R. Kuser, R. Fileto, C. Baudet, I. P. Pinto, A. J. Montagner, J. F. Palandrani, et al. 2004. Javaprotein dossier: A novel web-based data visualization tool for comprehensive analysis of protein structure. Nucleic Acids Research 32 (suppl 2):W595–W601.
  • Pandey, G., V. Kumar, and M. Steinbach. 2006. Computational approaches for protein function prediction: A survey. Technical report, TR06-028. University of Minnesota, Minneapolis, MN. Available in http://cs-dev.umn.edu/sites/cs.umn.edu/files/tech_reports/06-028.pdf. Accessed in 26-04-17
  • Quang, A. T., Q.-L. Zhang, and X. Li. 2002. Evolving support vector machine parameters. Proceedings of the First International Conference on Machine Learning and Cybernetics, Brijing, IEEE Computer Society Press, Silver Spring, MD, pp. 548–551. doi: 10.1109/ICMLC.2002.1176817.
  • Ren, Y., and G. Bai. 2010. Determination of optimal svm parameters by using ga/pso. Journal of Computers 5 (8):1160–68.
  • Resende, W. K., R. A. Nascimento, C. R. Xavier, I. F. Lopes, and C. Nobre. 2012. The use of support vector machine and genetic algorithms to predict protein function.. IEEE International Conference on Systems, Man, and Cybernetics (SMC), COEX, Seoul, Korea, 1773–1778. doi: 10.1109/ICSMC.2012.6377994
  • Rychetsky, M. 2001. Algorithms and architectures for machine learning based on regularized neural networks and support vector approaches. Berlin, Germany: Shaker Verlag GmbH.
  • Sangita, B, P., and S. R. Deshmukh. 2011. Use of support vector machine, decision tree and naive bayesian techniques for wind speed classification. Proceedings of the International Conference on Power and Energy Systems (ICPS), Chennai, 1–8. doi: 10.1109/ICPES.2011.6156687.
  • Shi, Y. 2004. Particle swarm optimization. IEEE Connections 2 (1):8–13.
  • Srinivas, M., and L. Patnaik. 1994. Genetic algorithms: A survey. Computer 27 (6):17–26.
  • Vapnik, V., and A. Lerner. 1963. Pattern recognition using generalized portrait method. Automation and Remote Control. 24 (6):774–80.
  • Zhou, J., O. O. Maruatona, and W. Wang. 2011. Parameter optimization for support vector machine classifier with IO-GA. Proceedings of the 1st International Workshop on Complexity and Data Mining (IWCDM), Nanjing, Jiangsu, 117–20. IEEE. doi: 10.1109/IWCDM.2011.34.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.