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Variance Based Particle Swarm Optimization for Function Optimization and Feature Selection

  • Yamuna PrasadEmail author
  • K. K. Biswas
  • M. Hanmandlu
  • Chakresh Kumar Jain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)

Abstract

Soft computing based techniques have been widely used in multi-objective optimization problems such as multi-modal function optimization, control and automation, network routing and feature selection etc. Feature Selection (FS) in high dimensional data can be modeled as multi-objective optimization problem to reduce the number of features while improving the overall accuracy. Generally, the traditional local optimization methods may not achieve this twin goal as there are many locally optimal solutions. Recently, various flavors of Particle Swarm Optimization (PSO) have been successfully applied for function optimization. The main issue in these variants of PSO is that it gets stuck in local optimum.

In this paper, we have developed a novel variant of PSO which controls the velocity of particles in a swarm. We have named the proposed method as Variance Particle Swarm Optimization (VPSO) henceforth. In VPSO, the velocity is influenced by the variance of the population. When the variance of the population is high, particles make use of exploitation and vice versa. This reduces the effect of swamping in local optimum. We have validated VPSO method for function optimization and feature selection. Our proposed VPSO method achieves significantly better results against the various PSO methods on eight publicly available benchmark functions optimization and on five publicly available benchmark datasets for feature selection.

Keywords

Function optimization Feature selection Support Vector Machine (SVM) Particle Swarm Optimization (PSO) 

References

  1. 1.
    Bekkerman, R., Yaniv, R.E., Tishby, N., Winter, Y.: Distributional word clusters vs. words for text categorization. J. Mach. Learn. Res. 3, 1183–1208 (2003)zbMATHGoogle Scholar
  2. 2.
    Belew, R.K., McInerney, J., Schraudolph, N.N.: Evolving networks: using the genetic algorithm with connectionist learning. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II, pp. 511–547. Addison-Wesley, Redwood City (1992)Google Scholar
  3. 3.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)zbMATHGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm
  5. 5.
    Chen, G., Zhang, X., Wang, Z.J., Li, F.: An enhanced artificial bee colony-based support vector machine for image-based fault detection. Math. Prob. Eng. 2015, 12 (2015)Google Scholar
  6. 6.
    Chuang, L.Y., Yang, C.H., Li, J.C.: Chaotic maps based on binary particle swarm optimization for feature selection. Appl. Soft Comput. 11(1), 239–248 (2011)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(23), 243–278 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)zbMATHGoogle Scholar
  9. 9.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  10. 10.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  11. 11.
    Huang, C.L.: ACO-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 73(1–3), 438–448 (2009)CrossRefGoogle Scholar
  12. 12.
    Tu, C.-J., Chuang, L.Y., Chang, J.Y., Yang, C.H.: Feature selection using PSO-SVM. IAENG Int. J. Comput. Sci. 33(1), 111–116 (2007)Google Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Khanesar, M., Teshnehlab, M., Shoorehdeli, M.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control Automation, MED 2007, pp. 1–6, June 2007Google Scholar
  15. 15.
    Kohavi, R., Becker, B., Sommerfield, D.: Improving simple Bayes. Silicon Graphics Inc., Mountain View, CA, Technical report, Data Mining and Visualization Group (1997)Google Scholar
  16. 16.
    Kumar, P.G., Victoire, A.T.A., Renukadevi, P., Devaraj, D.: Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm. Expert Syst. Appl. 39(2), 1811–1821 (2012)CrossRefGoogle Scholar
  17. 17.
    Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, pp. 140–144. AAAI Press (1994)Google Scholar
  18. 18.
    Liang, J., Qin, A., Suganthan, P., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  19. 19.
    Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)CrossRefGoogle Scholar
  20. 20.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRefGoogle Scholar
  21. 21.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005)CrossRefGoogle Scholar
  22. 22.
    Prasad, Y., Biswas, K.K.: PSO - SVM based classifiers: a comparative approach. In: Ranka, S., Banerjee, A., Biswas, K.K., Dua, S., Mishra, P., Moona, R., Poon, S.-H., Wang, C.-L. (eds.) IC3 2010. CCIS, vol. 94, pp. 241–252. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14834-7_23 CrossRefGoogle Scholar
  23. 23.
    Prasad, Y., Biswas, K.K., Jain, C.K.: SVM classifier based feature selection using GA, ACO and PSO for siRNA design. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6146, pp. 307–314. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13498-2_40 CrossRefGoogle Scholar
  24. 24.
    Tan, M., Wang, L., Tsang, I.W.: Learning sparse SVM for feature selection on very high dimensional datasets. In: Proceedings of the Twenty-Seventh International Conference on Machine Learning, pp. 1047–1054 (2010)Google Scholar
  25. 25.
    Varma, M., Babu, B.R.: More generality in efficient multiple kernel learning. In: Proceedings of the Twenty-Sixth International Conference on Machine Learning, pp. 1065–1072 (2009)Google Scholar
  26. 26.
    Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Vapnik, V.: Feature selection for SVMS. In: Advances in Neural Information Processing Systems (NIPS 2013), vol. 13, pp. 668–674 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yamuna Prasad
    • 1
    Email author
  • K. K. Biswas
    • 1
  • M. Hanmandlu
    • 1
  • Chakresh Kumar Jain
    • 2
  1. 1.Indian Institute of TechnologyNew DelhiIndia
  2. 2.Jaypee Institute of Information TechnologyNoidaIndia

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