A Binary Cuckoo Search and Its Application for Feature Selection

  • L. A. M. Pereira
  • D. Rodrigues
  • T. N. S. Almeida
  • C. C. O. Ramos
  • A. N. Souza
  • X.-S. Yang
  • J. P. Papa
Part of the Studies in Computational Intelligence book series (SCI, volume 516)


In classification problems, it is common to find datasets with a large amount of features, some of theses features may be considered as noisy. In this context, one of the most used strategies to deal with this problem is to perform a feature selection process in order to build a subset of features that can better represents the dataset. As feature selection can be modeled as an optimization problem, several studies have to attempted to use nature-inspired optimization techniques due to their large generalization capabilities. In this chapter, we use the Cuckoo Search (CS) algorithm in the context of feature selection tasks. For this purpose, we present a binary version of the Cuckoo Search, namely BCS, as well as we evaluate it with different transfer functions that map continuous solutions to binary ones. Additionally, the Optimum-Path Forest classifier accuracy is used as the fitness function. We conducted simulations comparing BCS with binary versions of the Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. BCS has obtained reasonable results when we consider the compared techniques for feature selection purposes.


Feature selection Pattern classification Meta-heuristic algorithms Optimum-path forest Cuckoo search algorithm 


  1. 1.
    Banati, H., Bajaj, M.: Fire fly based feature selection approach. Int. J. Comput. Sci. Issues 8(4), 473–480 (2011)Google Scholar
  2. 2.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)Google Scholar
  3. 3.
    Valian, E., Mohanna, S., Tavakoli, S.: On the mean accuracy of statistical pattern recognizers. Int. J. Artif. Intell. Appl. 2(3), 36–43 (2011)Google Scholar
  4. 4.
    Falcão, A., Stolfi, J., Lotufo, R.: The image foresting transform theory, algorithms, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 19–29 (2004)Google Scholar
  5. 5.
    Firpi, H.A., Goodman, E.: Swarmed feature selection. Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop, pp. 112–118. IEEE Computer Society, Washington, DC, USA (2004)Google Scholar
  6. 6.
    Gandomi, A., Yang, X.S., Alavi, A.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers 29(1), 17–35 (2013)Google Scholar
  7. 7.
    Geem, Z.W.: Music-Inspired Harmony Search Algorithm: Theory and Applications, 1st edn. Springer Publishing Company, Berlin (2009)Google Scholar
  8. 8.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)Google Scholar
  9. 9.
    Kaveh, A., Bakhshpoori, T.: Optimum design of steel frames using cuckoo search algorithm with lvy flights. The Structural Design of Tall and Special Buildings pp. n/a-n/a (2011).Google Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and. Cybernetics, vol. 5, pp. 4104–4108 (1997)Google Scholar
  12. 12.
    Layeb, A.: A novel quantum inspired cuckoo search for knapsack problems. Int. J. Bio-Inspired Comput. 3(5), 297–305 (2011)Google Scholar
  13. 13.
    Nakamura, R.Y.M., Pereira, C.R., Papa, J.P., Falcão, A.: Optimum-path forest pruning parameter estimation through harmony search. In: Proceedings of the 24th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 181–188. IEEE Computer Society, Washington, DC, USA (2011)Google Scholar
  14. 14.
    Papa, J., Pagnin, A., Schellini, S., Spadotto, A., Guido, R., Ponti, M., Chiachia, G., Falcão, A.: Feature selection through gravitational search algorithm. In: Proceedings of the 36th IEEE International Conference on Acoustics, Speech and, Signal Processing, pp. 2052–2055 (2011).Google Scholar
  15. 15.
    Papa, J.P., Falcão, A.X., Albuquerque, V.H.C., Tavares, J.M.R.S.: Efficient supervised optimum-path forest classification for large datasets. Pattern Recogn. 45(1), 512–520 (2012)CrossRefGoogle Scholar
  16. 16.
    Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. Int. J. Imaging Syst. Technol. 19(2), 120–131 (2009)CrossRefGoogle Scholar
  17. 17.
    Ramos, C., Souza, A., Chiachia, G., Falcão, A., Papa, J.: A novel algorithm for feature selection using harmony search and its application for non-technical losses detection. Comput. Electr. Eng. 37(6), 886–894 (2011)CrossRefGoogle Scholar
  18. 18.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefMATHGoogle Scholar
  19. 19.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9, 727–745 (2010)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Rodrigues, D., Pereira, L.A.M., Almeida, T.N.S., Ramos, C.C.O., Souza, A.N., Yang, X.S., Papa, J.P.: BCS: A binary cuckoo search algorithm for feature selection. In: Proceedings of the IEEE International Symposium on Circuits and Systems. Beijing, China (2013).Google Scholar
  21. 21.
    Senthilnath, J., Das, V., Omkar, S., Mani, V.: Clustering using levy flight cuckoo search. In: J.C. Bansal, P. Singh, K. Deep, M. Pant, A. Nagar (eds.) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Advances in Intelligent Systems and Computing, vol. 202, pp. 65–75. Springer India (2013)Google Scholar
  22. 22.
    Tein, L.H., Ramli, R.: Recent advancements of nurse scheduling models and a potential path. In: Proceedings of 6th IMT-GT conference on mathematics, statistics and its applications (ICMSA 2010) (2010)Google Scholar
  23. 23.
    Vazquez, R.: Training spiking neural models using cuckoo search algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 679–686 (2011)Google Scholar
  24. 24.
    Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature Biologically Inspired Computing (NaBIC 2009), pp. 210–214 (2009)Google Scholar
  25. 25.
    Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optimisation 1, 330–343 (2010)CrossRefMATHGoogle Scholar
  26. 26.
    Yang, X.S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 1–6 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • L. A. M. Pereira
    • 1
  • D. Rodrigues
    • 1
  • T. N. S. Almeida
    • 1
  • C. C. O. Ramos
    • 2
  • A. N. Souza
    • 3
  • X.-S. Yang
    • 4
  • J. P. Papa
    • 1
  1. 1.Department of ComputingUNESP—Univ Estadual PaulistaBauruBrazil
  2. 2.Department of Electrical EngineeringUniversity of São PauloSão PauloBrazil
  3. 3.Department of Electrical EngineeringUNESP—Univ Estadual PaulistaBauruBrazil
  4. 4.School of Science and TechnologyMiddlesex University, HendonLondonUK

Personalised recommendations