Skip to main content
Log in

Feature selection for high-dimensional classification using a competitive swarm optimizer

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript


When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance.Please check the edit made in the article title Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large-scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to perform feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO-based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others


  • Aha D, Kibler D, Albert M (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66

    Google Scholar 

  • Almuallim H, Dietterich TG (1994) Learning boolean concepts in the presence of many irrelevant features. Artif Intell 69(1):279–305

    Article  MathSciNet  MATH  Google Scholar 

  • Bache K, Lichman M (2013) UCI machine learning repository.

  • Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124

    Article  MathSciNet  MATH  Google Scholar 

  • Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatorial optimization. Nat Comput 8(2):239–287

    Article  MathSciNet  MATH  Google Scholar 

  • Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  • Chen WN, Zhang J, Lin Y, Chen N, Zhan ZH, Chung HSH, Li Y, Shi YH (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258

    Article  Google Scholar 

  • Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 31(3):226–233

    Article  Google Scholar 

  • Cheng R, Jin Y (2014) Demonstrator selection in a social learning particle swarm optimizer. In: 2014 IEEE congress on evolutionary computation, pp 3103–3110

  • Cheng R, Jin Y (2015) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204

    Article  Google Scholar 

  • Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60

    Article  MathSciNet  MATH  Google Scholar 

  • Chuang LY, Chang HW, Tu CJ, Yang CH (2008) Improved binary pso for feature selection using gene expression data. Comput Biol Chem 32(1):29–38

    Article  MATH  Google Scholar 

  • Chuang LY, Tsai SW, Yang CH (2011) Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst Appl 38(10):12699–12707

    Article  Google Scholar 

  • Fei H, Huan J (2010) Boosting with structure information in the functional space: an application to graph classification. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, pp 643–652

  • Fong S, Wong R, Vasilakos AV (2016) Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Trans Serv Comput 9(1):33–45

    Google Scholar 

  • Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recognit 43(1):5–13

    Article  MATH  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  • Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593

    Article  Google Scholar 

  • Hu M, Wu TF, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput 17(5):705–720

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks: proceedings, IS - SN -, vol 4, pp 1942–1948

  • Kira K, Rendell LA(1992) A practical approach to feature selection. In: Proceedings of the international workshop on machine learning, pp 249–256

  • Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273–324

    Article  MATH  Google Scholar 

  • Kwak N, Choi CH (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 13(1):143–159

    Article  Google Scholar 

  • Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210–224

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Liao JG, Chin KV (2007) Logistic regression for disease classification using microarray data: model selection in a large p and small n case. Bioinformatics 23(15):1945–1951

    Article  Google Scholar 

  • Lin SW, Chen SC (2009) PSOLDA: a particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis. Appl Soft Comput 9(3):1008–1015

    Article  MathSciNet  Google Scholar 

  • Liu Z, Jiang F, Tian G, Wang S, Sato F, Meltzer SJ, Tan M (2007) Sparse logistic regression with Lp penalty for biomarker identification. Stat Appl Gen Mol Biol 6(1):6. doi:10.2202/1544-6115.1248

  • Neshatian K, Zhang M(2009) Pareto front feature selection: using genetic programming to explore feature space. In: Proceedings of the annual conference on genetic and evolutionary computation, ACM, pp 1027–1034

  • Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, Heidelberg

    MATH  Google Scholar 

  • Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15(11):1119–1125

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence., The 1998 IEEE international conference on IS - SN - VO -, pp 69–73

  • Tan M, Tsang IW, Wang L (2013) Minimax sparse logistic regression for very high-dimensional feature selection. IEEE Trans Neural Netw Learn Syst 24(10):1609–1622

    Article  Google Scholar 

  • Tran B, Xue B, Zhang M (2016) Bare-bone particle swarm optimisation for simultaneously discretising and selecting features for high-dimensional classification. In: Squillero G, Burelli P (eds) Applications of evolutionary computation: 19th European conference, evoapplications 2016, Porto, Portugal, March 30–April 1, 2016, Proceedings, Part I, Springer International Publishing, pp 701–718

  • Unler A, Murat A (2010) A discrete particle swarm optimization method for feature selection in binary classification problems. Eur J Oper Res 206(3):528–539

    Article  MATH  Google Scholar 

  • Wang H, Sun H, Li C, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Whitney AW (1971) A direct method of nonparametric measurement selection. IEEE Trans Comput C–20(9):1100–1103

    Article  MATH  Google Scholar 

  • Xue B, Zhang M, Browne W, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput PP(99):1–1

    Google Scholar 

  • Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656–1671

    Article  Google Scholar 

  • Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput 18:261–276

    Article  Google Scholar 

  • Zhai Y, Ong YS, Tsang IW (2014) The emerging “Big Dimensionality”. IEEE Comput Intell Mag 9(3):14–26

    Article  Google Scholar 

  • Zhan ZH, Zhang J, Li Y, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381

    Article  Google Scholar 

  • Zhu Z, Ong YS, Dash M (2007) Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern Part B Cybern 37(1):70–76

    Article  Google Scholar 

Download references


This work was supported in part by National Natural Science Foundation of China (No. 71533001), the Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China (No. 61428302), and an EPSRC Grant (No. EP/M017869/1).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yaochu Jin.

Ethics declarations

Conflict of interest

S. Gu declares that he has no conflict of interest. R. Cheng declares that he has no conflict of interest. Y. Jin declares that he has no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, S., Cheng, R. & Jin, Y. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 22, 811–822 (2018).

Download citation

  • Published:

  • Issue Date:

  • DOI: