Skip to main content
SpringerLink
Log in

Overview of Particle Swarm Optimisation for Feature Selection in Classification

  • Conference paper
Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

Included in the following conference series:

Abstract

Feature selection is a process of selecting a subset of relevant features from a large number of original features to achieve similar or better classification performance and improve the computation efficiency. As an important data pre-processing technique, research into feature selection has been carried out over the past four decades. Determining an optimal feature subset is a complicated problem. Due to the limitations of conventional methods, evolutionary computation (EC) has been proposed to solve feature selection problems. Particle swarm optimisation (PSO) is an EC technique which recently has caught much interest from researchers in the field. This paper presents a review of PSO for feature selection in classification. After describing the background of feature selection and PSO, recent work involving PSO for feature selection is reviewed. Current issues and challenges are also presented for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alba, E., Garcia-Nieto, J., Jourdan, L., Talbi, E.G.: Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 284–290 (2007)

    Google Scholar 

  2. Aneesh, M., Masand, A.A., Manikantan, K.: Optimal feature selection based on image pre-processing using accelerated binary particle swarm optimization for enhanced face recognition. Procedia Engineering 30(5), 750–758 (2012)

    Article  Google Scholar 

  3. Azevedo, G., Cavalcanti, G., Filho, E.: An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3577–3584 (2007)

    Google Scholar 

  4. Cervante, L., Xue, B., Shang, L., Zhang, M.: Binary particle swarm optimisation and rough set theory for dimension reduction in classification. In: IEEE Congress on Evolutionary Computation, pp. 2428–2435 (2013)

    Google Scholar 

  5. Cervante, L., Xue, B., Shang, L., Zhang, M.: A multi-objective feature selection approach based on binary pso and rough set theory. In: Middendorf, M., Blum, C. (eds.) EvoCOP 2013. LNCS, vol. 7832, pp. 25–36. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  6. Cervante, L., Xue, B., Zhang, M., Shang, L.: Binary particle swarm optimisation for feature selection: A filter based approach. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp. 881–888 (2012)

    Google Scholar 

  7. Chakraborty, B.: Feature subset selection by particle swarm optimization with fuzzy fitness function. In: 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008), pp. 1038–1042 (2008)

    Google Scholar 

  8. Chakraborty, B., Chakraborty, G.: Fuzzy consistency measure with particle swarm optimization for feature selection. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013), pp. 4311–4315 (2013)

    Google Scholar 

  9. 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)

    Article  MATH  Google Scholar 

  10. Chuang, L.Y., Tsai, S.W., Yang, C.H.: Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst. Appl. 38(10), 12699–12707 (2011)

    Article  Google Scholar 

  11. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1(4), 131–156 (1997)

    Article  Google Scholar 

  12. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43 (October 1995)

    Google Scholar 

  13. Eberhart, R., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: IEEE Congress on Evolutionary Computation, vol. 1, pp. 81–86 (2001)

    Google Scholar 

  14. Guan, J., Han, F., Yang, S.: A new gene selection method for microarray data based on PSO and informativeness metric. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.) ICIC 2013. LNCS, vol. 7996, pp. 145–154. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Huang, C.L., Dun, J.F.: A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Appl. Soft Comput. 8, 1381–1391 (2008)

    Article  Google Scholar 

  16. Ke, L., Feng, Z., Xu, Z., Shang, K., Wang, Y.: A multiobjective ACO algorithm for rough feature selection. In: Second Pacific-Asia Conference on Circuits,Communications and System (PACCS 2010), vol. 1, pp. 207–210 (2010)

    Google Scholar 

  17. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  18. Kennedy, J., Eberhart, R.: 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 

  19. Kennedy, J., Spears, W.: Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: IEEE World Congress on Computational Intelligence, pp. 78–83 (1998)

    Google Scholar 

  20. Lane, M., Xue, B., Liu, I., Zhang, M.: Particle swarm optimisation and statistical clustering for feature selection. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS, vol. 8272, pp. 214–220. Springer, Heidelberg (2013)

    Google Scholar 

  21. Lane, M., Xue, B., Liu, I., Zhang, M.: Gaussian based particle swarm optimisation and statistical clustering for feature selection. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 133–144. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  22. Langley, P.: Selection of relevant features in machine learning. In: AAAI Technique Report FS-94-02, pp. 127–131 (October 1994)

    Google Scholar 

  23. Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35(4), 1817–1824 (2008)

    Article  Google Scholar 

  24. Liu, H., Dougherty, E., Dy, J., Torkkola, K., Tuv, E., Peng, H., Ding, C., Long, F., Berens, M., Parsons, L., Zhao, Z., Yu, L., Forman, G.: Evolving feature selection. IEEE Intelligent Systems 20(6), 64–76 (2005)

    Article  Google Scholar 

  25. Mohamad, M., Omatu, S., Deris, S., Yoshioka, M., Abdullah, A., Ibrahim, Z.: An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes. Algorithms for Molecular Biology 8(1), 15 (2013)

    Article  Google Scholar 

  26. Mohamad, M., Omatu, S., Deris, S., Yoshioka, M.: A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data. Information Technology in Biomedicine 15(6), 813–822 (2011)

    Article  Google Scholar 

  27. Mohemmed, A., Zhang, M., Johnston, M.: Particle Swarm Optimization based Adaboost for face detection. In: IEEE Congress on Evolutionary Computation, pp. 2494–2501 (2009)

    Google Scholar 

  28. Narendra, P., Fukunaga, K.: A Branch and Bound Algorithm for Feature Subset Selection. IEEE Transactions on Computers C-26(9), 917–922 (1977)

    Article  Google Scholar 

  29. Neshatian, K., Zhang, M., Andreae, P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Transactions on Evolutionary Computation 16(5), 645–661 (2012)

    Article  Google Scholar 

  30. Oliveira, L., Sabourin, R., Bortolozzi, F., Suen, C.: Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: 16th International Conference on Pattern Recognition, vol. 1, pp. 568–571 (2002)

    Google Scholar 

  31. Pal, S., Chakraborty, B.: Fuzzy Set Theoretic Measure for Automatic Feature Evaluation. IEEE Transactions on Systems, Man and Cybernetics 16(5), 754–760 (1986)

    Article  Google Scholar 

  32. Purohit, A., Chaudhari, N., Tiwari, A.: Construction of classifier with feature selection based on genetic programming. In: IEEE Congress on Evolutionary Computation, pp. 1–5 (2010)

    Google Scholar 

  33. Sahu, B., Mishra, D.: A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data. Procedia Engineering 38(0), 27–31 (2012)

    Article  Google Scholar 

  34. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE Congress on Evolutionary Computation, pp. 69–73 (1998)

    Google Scholar 

  35. Sivagaminathan, R.K., Ramakrishnan, S.: A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Systems with Applications 33(1), 49–60 (2007)

    Article  Google Scholar 

  36. Stevanovic, A., Xue, B., Zhang, M.: Feature selection based on pso and decision-theoretic rough set model. In: IEEE Congress on Evolutionary Computation, pp. 2840–2847 (2013)

    Google Scholar 

  37. Subbotin, S., Oleynik, A.: The multi objective evolutionary feature selection. In: International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science, pp. 115–116 (2008)

    Google Scholar 

  38. Unler, A., Alper Murat, R.B.C.: mr2pso: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Information Sciences 20, 4625–4641 (2011)

    Article  Google Scholar 

  39. Unler, A., Murat, A.: A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research 206(3), 528–539 (2010)

    Article  MATH  Google Scholar 

  40. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28(4), 459–471 (2007)

    Article  Google Scholar 

  41. Waqas, K., Baig, R., Ali, S.: Feature subset selection using multi-objective genetic algorithms. In: 13th International Conference on INMIC, pp. 1–6 (2009)

    Google Scholar 

  42. Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang, M.: Binary PSO and rough set theory for feature selection: A multi-objective filter based approach. International Journal of Computational Intelligence and Applications 13(02), 1450009 (2014)

    Article  Google Scholar 

  43. Xue, B., Cervante, L., Shang, L., Browne, W., Zhang, M.: A multi-objective particle swarm optimisation for filter-based feature selection in classification problems. Connect. Sci. 24(2-3), 91–116 (2012)

    Article  Google Scholar 

  44. Xue, B., Zhang, M., Browne, W.N.: Multi-objective particle swarm optimisation for feature selection. In: Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, PA, USA, pp. 81–88. ACM (2012)

    Google Scholar 

  45. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing 18(0), 261–276 (2014)

    Article  Google Scholar 

  46. Xue, B., Zhang, M., Browne, W.N.: Novel initialisation and updating mechanisms in PSO for feature selection in classification. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 428–438. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  47. Xue, B., Zhang, M., Browne, W.: New fitness functions in binary particle swarm optimisation for feature selection. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)

    Google Scholar 

  48. 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)

    Article  Google Scholar 

  49. Yang, C.S., Chuang, L.Y., Ke, C.H., Yang, C.H.: Boolean binary particle swarm optimization for feature selection. In: IEEE Congress on Evolutionary Computation (CEC 2008), pp. 2093–2098 (2008)

    Google Scholar 

  50. Yang, C.S., Chuang, L.Y., Li, J.C., Yang, C.H.: Chaotic maps in binary particle swarm optimization for feature selection. In: IEEE Conference on Soft Computing in Industrial Applications, pp. 107–112 (2008)

    Google Scholar 

  51. Yong, Z., Dunwei, G., Ying, H., Wanqiu, Z.: Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148(0), 150–157 (2015)

    Google Scholar 

  52. Yu, X.M., Xiong, X.Y., Wu, Y.W.: A PSO-based approach to optimal capacitor placement with harmonic distortion consideration. Electric Power Systems Research 71(1), 27–33 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand

    Binh Tran, Bing Xue & Mengjie Zhang

Authors
  1. Binh Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Tran, B., Xue, B., Zhang, M. (2014). Overview of Particle Swarm Optimisation for Feature Selection in Classification. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13563-2_51

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation