Advertisement

A New Binary Particle Swarm Optimisation Algorithm for Feature Selection

  • Bing XueEmail author
  • Su Nguyen
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

Feature selection aims to select a small number of features from a large feature set to achieve similar or better classification performance than using all features. This paper develops a new binary particle swarm optimisation (PSO) algorithm (named PBPSO) based on which a new feature selection approach (PBPSOfs) is developed to reduce the number of features and increase the classification accuracy. The performance of PBPSOfs is compared with a standard binary PSO based feature selection algorithm (BPSOfs) and two traditional feature selection algorithms on 14 benchmark problems of varying difficulty. The results show that PBPSOfs can be successfully used for feature selection to select a small number of features and improve the classification performance over using all features. PBPSOfs further reduces the number of features selected by BPSOfs and simultaneously increases the classification accuracy, especially on datasets with a large number of features. Meanwhile, PBPSOfs achieves better performance than the two traditional feature selection algorithms. In addition, the results also show that PBPSO as a general binary optimisation technique can achieve better performance than standard binary PSO and uses less computational time.

Keywords

Binary particle swarm optimisation Feature selection Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Caruana, R., Freitag, D.: Greedy attribute selection. In: International Conference on Machine Learning (ICML 1994), pp. 28–36. Morgan Kaufmann (1994)Google Scholar
  2. 2.
    Chuang, L.Y., Chang, H.W., Tu, C.J., Yang, C.H.: Improved binary PSO for feature selection using gene expression data. Computational Biology and Chemistry 32(29), 29–38 (2008)CrossRefzbMATHGoogle Scholar
  3. 3.
    Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1(4), 131–156 (1997)CrossRefGoogle Scholar
  4. 4.
    Engelbrecht, A.P.: Computational intelligence: an introduction, 2nd edn. Wiley (2007)Google Scholar
  5. 5.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  6. 6.
    Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009), pp. 332–339 (2009)Google Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11, 931–934 (2009)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics (1997), Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)Google Scholar
  10. 10.
    Khanesar, M., Teshnehlab, M., Shoorehdeli, M.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control Automation (MED 2007), pp. 1–6 (2007)Google Scholar
  11. 11.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  12. 12.
    Marill, T., Green, D.: On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory 9(1), 11–17 (1963)CrossRefGoogle Scholar
  13. 13.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation (CEC 1998), pp. 69–73 (1998)Google Scholar
  14. 14.
    Sudholt, D., Witt, C.: Runtime analysis of binary PSO. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO 2008), pp. 135–142. ACM, New York (2008)Google Scholar
  15. 15.
    Whitney, A.: A direct method of nonparametric measurement selection. IEEE Transactions on Computers C-20(9), 1100–1103 (1971)Google Scholar
  16. 16.
    Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang, M.: A multi-objective particle swarm optimisation for filter based feature selection in classification problems. Connection Science (2012)Google Scholar
  17. 17.
    Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp. 2145–2152 (2012)Google Scholar
  18. 18.
    Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics 43(6), 1656–1671 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations