A Harmony Search Based Gradient Descent Learning-FLANN (HS-GDL-FLANN) for Classification

  • Bighnaraj Naik
  • Janmenjoy Nayak
  • H. S. Behera
  • Ajith Abraham
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

The Harmony Search (HS) algorithm is meta-heuristic optimization inspired by natural phenomena called musical process and it quite simple due to few mathematical requirements and simple steps as compared to earlier meta-heuristic optimization algorithms. It mimics the local and global search procedure of pitch adjustment during production of pleasant harmony by musicians. Although HS has been used in many application like vehicle routing problems, robotics, power and energy etc., in this paper, an attempt is made to design a hybrid FLANN with Harmony Search based Gradient Descent Learning for classification. The proposed algorithm has been compared with FLANN, GA based FLANN and PSO based FLANN classifier to get remarkable performance. All the four classifier are implemented in MATLAB and tested by couples of benchmark datasets from UCI machine learning repository. Finally, to get generalized performance, 5 fold cross validation is adopted and result are analyzed under one-way ANOVA test.

Keywords

Data mining Machine learning Classification Harmony search Functional link artificial neural network Gradient descent learning 

References

  1. 1.
    Geem, Z.W., et al.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  2. 2.
    Geem, Z.W.: School bus routing using harmony search. In: Genetic and Evolutionary Computation Conference, Washington, DC (2005)Google Scholar
  3. 3.
    Geem, Z.W., et al.: Harmony search for generalized orienteering problem: best touring in China. Lect. Notes Comput. Sci. 3612, 741–750 (2005)CrossRefGoogle Scholar
  4. 4.
    Geem, Z.W.: Optimal cost design of water distribution networks using harmony search. Eng. Optim. 38, 259–280 (2006)CrossRefGoogle Scholar
  5. 5.
    Yazdi, E. et.al.: A new biped locomotion involving arms swing based on neural network with harmony search optimizer. In: IEEE International Conference on Automation and Logistics, pp. 18–23 (2011)Google Scholar
  6. 6.
    Xu, H., et al.: Harmony search optimization algorithm: application to a reconfigurable mobile robot prototype. Stud. Comput. Intell. 270, 11–22 (2011)CrossRefGoogle Scholar
  7. 7.
    Tangpattanakul, P., et al.: Optimal trajectory of robot manipulator using harmony search algorithms. Stud. Comput. Intell. 270, 23–36 (2010)CrossRefGoogle Scholar
  8. 8.
    Coelho, L.S., et al.: A harmony search approach using exponential probability distribution applied to fuzzy logic control optimization. Stud. Comput. Intell. 270, 77–88 (2010)CrossRefGoogle Scholar
  9. 9.
    Das Sharma, K., et al.: Design of a hybrid stable adaptive fuzzy controller employing lyapunov theory and harmony search algorithm. IEEE Trans. Contr. Syst. Tech. 18, 1440–1447 (2010)Google Scholar
  10. 10.
    Javaheri, H., Goldoost-Soloot, R.: Locating and sizing of series facts devices using lineoutage sensitivity factors and harmony search algorithm. In: 2nd International Conference on Advances in Energy Engineering, vol. 14, pp. 1445–1450 (2012)Google Scholar
  11. 11.
    Sirjani, R., et al.: Optimal allocation of shuntvar compensators in power systems using a novel global harmony search algorithm. Int. J. Electr. Power Energy Syst. 43(1), 562–572 (2012)CrossRefGoogle Scholar
  12. 12.
    Afshari, S., et al.: Application of an improved harmony search algorithm in well placement optimization using streamline simulation. J. Petrol. Sci. Eng. 78(3–4), 664–678 (2011)CrossRefGoogle Scholar
  13. 13.
    Geem, Z.W.: Discussion on combined heat and power economic dispatch by harmony search algorithm. Int. J. Electr. Power Energy Syst. 33(7), 1348 (2011)CrossRefGoogle Scholar
  14. 14.
    Panchal, A.: Harmony search in the rapeutic medical physics. Stud. Comput. Intell. 191, 189–203 (2009)CrossRefGoogle Scholar
  15. 15.
    Gandhi, T.K., et al.: Discrete harmony search based expert model for epileptic seizure detection in electroencephalography. Expert Syst. Appl. 39(4), 4055–4063 (2012)CrossRefGoogle Scholar
  16. 16.
    Landa-Torres, I., et al.: A multi-objective grouping harmony search algorithm for the optimal distribution of 24-hour medical emergency units. Expert Syst. Appl. 40(6), 2343–2349 (2012)CrossRefGoogle Scholar
  17. 17.
    Shariatkhah, M.H., et al.: Duration based reconfiguration of electric distribution networks using dynamic programming and harmony search algorithm. Int. J. Electr. Power Energy Syst. 41(1), 1–10 (2012)CrossRefGoogle Scholar
  18. 18.
    Degertekin, S.O.: Improved harmony search algorithms for sizing optimization of truss structures. Comput. Struct. 92–93, 229–241 (2012)CrossRefGoogle Scholar
  19. 19.
    Askarzadeh, A., Rezazadeh, A.: An innovative global harmony search algorithm for parameter identification of a PEM fuel cell model. IEEE Trans. Ind. Electron. 59(9), 3473–3480 (2012)CrossRefGoogle Scholar
  20. 20.
    Manjarres, D., et al.: A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26, 1818–1831 (2013)CrossRefGoogle Scholar
  21. 21.
    Klaseen, M., Pao, Y.H.: The functional link net in structural pattern recognition. In: IEEE Region 10 Conference on Computer and Communication Systems, vol. 2, pp. 567–571 (1990)Google Scholar
  22. 22.
    Liu, L. M. et al.: Image classification in remote sensing using functional link neural networks in Image analysis and interpretation. In: Proceedings of the IEEE Southwest Symposium, pp. 54–58 (1994)Google Scholar
  23. 23.
    Raghu, P.P., et al.: A combined neural network approach for texture classification. Neural Networks 8, 975–987 (1995)CrossRefGoogle Scholar
  24. 24.
    Patra, J.C., Pal, N.R.: A functional link artificial neural network for adaptive channel equalization. Sig. Process. 43, 181–195 (1995)CrossRefMATHGoogle Scholar
  25. 25.
    Teeter, J., Mo-Yuen, C.: Application of functional link neural network to HVAC thermal dynamic system identification. IEEE Trans. Industr. Electron. 45, 170–176 (1998)CrossRefGoogle Scholar
  26. 26.
    Park, G.H., Pao, Y.H.: Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net. Neurocomputing 31, 45–65 (2000)CrossRefGoogle Scholar
  27. 27.
    Patra, J.C., Kot, A.C.: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 32, 505–511 (2002)CrossRefGoogle Scholar
  28. 28.
    Abu-Mahfouz, I.-A.: A comparative study of three artificial neural networks for the detection and classification of gear faults. Int. J. Gen Syst. 34, 261–277 (2005)CrossRefMathSciNetMATHGoogle Scholar
  29. 29.
    Patra, J.C. et.al.: Financial prediction of major indices using computational efficient artificial neural networks. In: International Joint Conference on Neural Networks, Canada, pp. 2114–2120, 16–21 July 2006Google Scholar
  30. 30.
    Mishra, B.B., Dehuri, S.: Functional link artificial neural network for classification task in data mining. J. Comput. Sci. 3(12), 948–955 (2007)CrossRefGoogle Scholar
  31. 31.
    Dehuri, S., et al.: Genetic feature selection for optimal functional link artificial neural network in classification, pp. 156–163. Springer, Berlin (2008)Google Scholar
  32. 32.
    Patra, J.C. et.al.: Computationally efficient FLANN-based intelligent stock price prediction system. In: Proceedings of International Joint Conference on Neural Networks, IEEE, Atlanta, Georgia, USA, pp. 2431–2438, 14–19 June 2009Google Scholar
  33. 33.
    Abbas, H.M.: System identification using optimally designed functional link networks via a fast orthogonal search technique. J. Comput. 4, 147 (2009)CrossRefGoogle Scholar
  34. 34.
    Sun, J. et.al.: Functional link artificial neural network-based disease gene prediction. In: Proceedings of International Joint Conference on Neural Networks, IEEE, Atlanta, Georgia, USA, pp. 3003–3010, 14–19 June 2009Google Scholar
  35. 35.
    Nanda, S.J. et al.: Improved identification of nonlinear mimo plants using new hybrid FLANN-AIS model. In: International on Conference Advance Computing, pp. 141–146 (2009)Google Scholar
  36. 36.
    Chakravarty, S., Dash, P.K.: Forecasting stock market indices using hybrid network. In: World Congress on Nature & Biologically Inspired Computing, IEEE, pp. 1225–1230 (2009)Google Scholar
  37. 37.
    Majhi, R., et al.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Syst. Appl. 36, 6800–6808 (2009)CrossRefGoogle Scholar
  38. 38.
    Patra, J.C., Bornand, C.: Nonlinear dynamic system identification using Legendre neural network. Imn: International Joint Conference on Neural Networks, pp. 1–7 (2010)Google Scholar
  39. 39.
    Emrani, S. et al.: Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems. In: 5th IEEE Conference on Industrial Electronics and Applications, pp. 35–40 (2010)Google Scholar
  40. 40.
    Majhi, R. et.al.: Classification of consumer behavior using functional link artificial neural network. In: International Conference on Advances in Computer Engineering, IEEE, pp. 323–325 (2010)Google Scholar
  41. 41.
    Bebarta, D.K. et.al.: Forecasting and classification of indian stocks using different polynomial functional link artificial neural networks. In: 2012 Annual IEEE India Conference (INDICON), pp. 178–182 (2012)Google Scholar
  42. 42.
    Mili, F., Hamdi, H.: A comparative study of expansion functions for evolutionary hybrid functional link artificial neural networks for data mining and classification, pp. 1–8 (2013)Google Scholar
  43. 43.
    Mishra, S., et al.: A new meta-heuristic bat inspired classification approach for microarray data. Procedia Technol. 4, 802–806 (2012). (C3IT)CrossRefGoogle Scholar
  44. 44.
    Mahapatra, R. et.al.: Reduced feature based efficient cancer classification using single layer neural network. In: 2nd International Conference on Communication, Computing & Security. Procedia Technol. 6, 180–187 (2012)Google Scholar
  45. 45.
    Mishra, S. et.al.: An enhanced classifier fusion model for classifying biomedical data. Int. J. Comput. Vis. Robot. 3(½), 129–137 (2012)Google Scholar
  46. 46.
    Dehuri, S. et.al.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J. Syst, Softw. 85, 1333–1345 (2012)Google Scholar
  47. 47.
    Naik, B., Nayak, J., Behera, H.S.: A novel FLANN with a hybrid PSO and GA based gradient descent learning for classification. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing (FICTA), vol. 1. Advances in Intelligent Systems and Computing 327, pp. 745–754 (2014). doi:10.1007/978-3-319-11933-5_84
  48. 48.
    Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25, 76–79 (1992)CrossRefGoogle Scholar
  49. 49.
    Bache, K., Lichman, M.: UCI machine learning repository. http://archive.ics.uci.edu/ml. University of California, School of Information and Computer Science, Irvine, CA (2013)
  50. 50.
    Alcalá-Fdez, J., et al.: KEEL data-mining software tool: data set repository. Integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2–3), 255–287 (2011)Google Scholar
  51. 51.
    Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975). ISBN 9780262581110Google Scholar
  52. 52.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Network, vol.4, pp. 1942–1948 (1995)Google Scholar
  53. 53.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science (MHS), pp. 39–43 (1995)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Bighnaraj Naik
    • 1
  • Janmenjoy Nayak
    • 1
  • H. S. Behera
    • 1
  • Ajith Abraham
    • 2
    • 3
  1. 1.Department of Computer Science Engineering and Information TechnologyVeer Surendra Sai University of TechnologySambalpurIndia
  2. 2.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA
  3. 3.IT4Innovations—Center of ExcellenceVSB—Technical University of OstravaOstravaCzech Republic

Personalised recommendations