An opposition-based social spider optimization for feature selection

  • Rehab Ali Ibrahim
  • Mohamed Abd Elaziz
  • Diego Oliva
  • Erik Cuevas
  • Songfeng LuEmail author
Methodologies and Application


In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems.


Meta-heuristic (MH) Social spider optimization (SSO) Opposition-based learning (OBL) Feature selection (FS) 



This work is supported by the Science and Technology Program of Shenzhen of China under Grant Nos. JCYJ20170818160208570 and JCYJ20170307160458368. This study was carried out without any funding sources.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict interest.


  1. Akin A, Aydogdu I, Bilir T (2016) Cost and CO2 optimization for RC short column sections subjected to axial load and uniaxial/biaxial bending using the social spider optimization algorithm. In: Proceedings of the sustainable construction materials and technologies SCMTGoogle Scholar
  2. Al-Ani A, Khushaba RN, Al-Jumaily A (2011) Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst Appl 38(9):11515–11526Google Scholar
  3. Alba E, Nieto JG, Apolloni J (2009) Hybrid DE-SVM approach for feature selection: application to gene expression datasets. In: Proceedings of 2nd international logistics industrial information, pp 1–6Google Scholar
  4. Alsukker A, Al-Ani A, Khushaba RN (2013) Feature subset selection using differential evolution and a wheel based search strategy. Swarm Evol Comput 9:15–26Google Scholar
  5. Aparicio-Navarro FJ, Kyriakopoulos KG, Parish DJ (2014) Automatic dataset labelling and feature selection for intrusion detection systems. In: Proceedings—IEEE military communications conference MILCOM, pp 46–51Google Scholar
  6. Azar A, Inbarani H, Bagyamathi M (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl 23(1):55–72Google Scholar
  7. Bajaj M, Banati H (2011) Fire fly based feature selection approach. Int J Comput Sci 8(4):473–480Google Scholar
  8. Boggia R, Leardi R, Terrile M (1992) Genetic algorithms as a strategy for feature-selection. J Chemomet 6:267–281Google Scholar
  9. Browne WN, Xue B, Zhang M, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626Google Scholar
  10. Bulatovic D, Novakovic J, Strbac P (2011) Toward optimal feature selection using ranking methods and classification algorithms. Yugosl J Oper Res 21:119–135MathSciNetzbMATHGoogle Scholar
  11. Chen HL, Dong H, Zhu XD, Liu YN, Wang G, Wang SJ (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8:191–200Google Scholar
  12. Chen L-F, Chao-Ton S, Chen K-H, Wang P-C (2012a) Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis. Neural Comput Appl 21(8):2087–2096Google Scholar
  13. Cuevas E, Oliva D, Zaldivar D, Pérez-Cisneros M, Pajares G (2012) Opposition-based electromagnetism-like for global optimization. Int J Innov Comput Inf Control 8:8181–8198Google Scholar
  14. Chen Y-C, Pal NR, Chung I-F (2012b) An integrated mechanism for feature selection and fuzzy rule extraction for classification. IEEE Trans Fuzzy Syst 20(4):683–698Google Scholar
  15. Chen KH, Chen LF, Su CT, Wang PC (2012c) Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis. Neural Comput Appl 21(9):2087–2096Google Scholar
  16. Dash M, Liu H (1997) Feature selection for classification. Intell. Data Anal. 1(1–4):131–156Google Scholar
  17. De Jong K, Kamath ASU, Shehu A (2014) Effective automated feature construction and selection for classification of biological sequences. PLoS ONE 9(7):e99982Google Scholar
  18. El Aziz MA, Hassanien AE (2016) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 29:925–934Google Scholar
  19. El Aziz MA, Khidr W (2015) Nonnegative matrix factorization based on projected hybrid conjugate gradient algorithm. Signal Image Video Process 9(8):1825–1831Google Scholar
  20. El Aziz MA, Hassanien AE (2018) An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem. Neural Comput Appl 30(8):2441–2452Google Scholar
  21. El Aziz MA, Ewees AA, Elhoseny M (2017) Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In: 2017 8th international conference on computing, communication and networking technologies (ICCCNT)Google Scholar
  22. Fernandes EMGP, Azad MAK, Rocha AMAC (2014) Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems. Swarm Evolut Comput 14:66–75Google Scholar
  23. Frank A, Asuncion A (2010) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA.
  24. Gao H-H, Yang H-H, Wang X-Y (2005) Ant colony optimization based network intrusion feature selection and detection. In: 2005 international conference on machine learning and cybernetics, vol 6. IEEE, pp 3871–3875Google Scholar
  25. George DC, Roberto HW, Renato FC (2012) A global-ranking local feature selection method for text categorization. Expert Syst Appl 39(17):12851–12857Google Scholar
  26. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  27. Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Suganthan PN, Tiwari S (2005) Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Nanyang Technological University, Singapore and KanGAL, Kanpur Genetic Algorithms Laboratory, IIT, Kanpur, India, Technical Report, Rep. No., 2005Google Scholar
  28. Hart PE, Stork DG, Duda RO (2012) Pattern classification. Wiley, New YorkzbMATHGoogle Scholar
  29. Huang CL, Wang CJ (2006) A ga-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240Google Scholar
  30. Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9(7):1483–1492Google Scholar
  31. Jing S-Y (2014) A hybrid genetic algorithm for feature subset selection in rough set theory. Soft Comput 18(7):1373–1382Google Scholar
  32. Jothi G, Inbarani HH, Azar AT (2014) Supervised hybrid feature selection based on pso and rough sets for medical diagnosis. Comput Methods Programs Biomed 113:175–185Google Scholar
  33. Karanpreet K, Abrol P, Gupta S (2015) Social spider cloud web algorithm (SSCWA): a new meta-heuristic for avoiding premature convergence in cloud. Int J Innov Res Comput Commun Eng 3(6)Google Scholar
  34. Kononenko I (1994) Estimating attributes: analysis and extensions of relief. In: Machine learning: ECML-94. Springer, pp 171–182Google Scholar
  35. Kusban M, Susanto A, Wahyunggoro O (2016) Feature extraction for palmprint recognition using kernel-PCA with modification in Gabor parameters. In: 2016 1st international conference on biomedical engineering (IBIOMED), pp 1–6Google Scholar
  36. Lee J-S, Oh I-S, Moon B-R (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(1):424–1437Google Scholar
  37. Li Z, Han-J, Gu, Q (2011) Generalized fisher score for feature selection. In: Proceedings of the 27th conference on uncertainty in artificial intelligence (UAI), Barcelona, SpainGoogle Scholar
  38. Liu H, Motoda H (2007) Computational methods of feature selection. Chapman and Hall/CRC Press, CambridgezbMATHGoogle Scholar
  39. Luo Q, Abdel-Basset M, Zhou Y, Zhou Y (2017) A simplex method-based social spider optimization algorithm for clustering analysis. Eng Appl Artif Intell 64:67–82Google Scholar
  40. Meesad P, Unger H, Long N, Cong N (2014) Attribute reduction based on rough sets and the discrete firefly algorithm. Recent Adv Inf Commun Technol 265:13–22Google Scholar
  41. Milačić L, Jović S, Vujović T, Miljković J (2017) Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A Stat Mech Appl 465:285–288MathSciNetzbMATHGoogle Scholar
  42. Ming H (2008) A rough set based hybrid method to feature selection. In: International symposium on knowledge acquisition and modeling. IEEE, pp 585–588Google Scholar
  43. Mirhosseini M, Nezamabadi-pour H (2018) BICA: a binary imperialist competitive algorithm and its application in CBIR systems. Int J Mach Learn Cyber 9(12):2043–2057Google Scholar
  44. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133Google Scholar
  45. Mirjalili SZ, Saremi S, Mirjalili SM (2015) Designing evolutionary feedforward neural networks using social spider optimization algorithm. Neural Comput Appl 26(8):1919–1928Google Scholar
  46. Mohamed OA, EL-Sayed Waheed M, Abd El-aziz ME (2011) Mixture of generalized gamma density-based score function for fastica. Math Probl EngGoogle Scholar
  47. Nezamabadi-pour H, Kashef S (2015) An advanced ACO algorithm for feature subset selection. Neurocomputing 147(5):271–279Google Scholar
  48. Nezamabadi-pour H, Barani F, Mirhosseini M (2017) Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Appl Intell 47:304–318Google Scholar
  49. Peng J, Robila S (2007) Weighted additive criterion for linear dimension reduction. In: Seventh IEEE international conference on data mining (ICDM 2007), pp 619–624Google Scholar
  50. Phillips P, Ji G, Zhang Y, Wang S (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Orig Res Artic Knowl Based Syst 64:22–31Google Scholar
  51. Reddy AV, Janet B, Chandran TR (2017) Text clustering quality improvement using a hybrid social spider optimization. Int J Appl Eng Res 12(6):995–1008Google Scholar
  52. Saha S, Uryupina O, Sikdar UK, Ekbal A, Poesio M (2015) Differential evolution-based feature selection technique for anaphora resolution. Soft Comput 19:2149–2161Google Scholar
  53. Saryazdi S, Rashedi E, Nezamabadipour H (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHGoogle Scholar
  54. Saryazdi S, Rashedi E, Nezamabadi-pour H (2010) BGSA: binary gravitational search algorithm. Nat Comput 9:727–745MathSciNetzbMATHGoogle Scholar
  55. Stoffel K, Raileanu LE (2004) Theoretical comparison between the Gini index and information gain criteria. Ann Math Artif Intell 41(1):77–93MathSciNetzbMATHGoogle Scholar
  56. Stutzle T, Dorigo M, Birattari M (2006) Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Comput Intell Mag 1(4):28–39Google Scholar
  57. Su CT, Lin HC (2011) Applying electromagnetism-like mechanism for feature selection. Inf Sci 181(5):972–986Google Scholar
  58. Tang F, Liu Y, Zeng Z (2015) Feature selection based on dependency margin. IEEE Trans Cybernet 45(6):1209–1221Google Scholar
  59. Teng X, Xia W, Wang X, Yang J, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28(4):459–471Google Scholar
  60. Thanushkodi K, Suguna N (2011) An independent rough set approach hybrid with artificial bee colony algorithm for dimensionality reduction. Am J Appl Sci 8(3):261–266Google Scholar
  61. Tibshirani R, Hastie T, Friedman J (2001) The elements of statistical learning. Springer, BerlinzbMATHGoogle Scholar
  62. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. Int Conf Comput Intell Model Control Autom Int Conf Intell Agents Web Technol Internet Commer 1:695–701Google Scholar
  63. Vo MC, Dang BT, Truong TK (2017) Social spider algorithm-based spectrum allocation optimization for cognitive radio networks. Int J Appl Eng Res 12(13):3879–3887Google Scholar
  64. Wang S-H, Zhang Y, Li Y-J, Jia W-J, Liu F-Y, Yang M-M, Zhang Y-D (2018) Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimedia Tools Appl 77(9):10393–10417Google Scholar
  65. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83Google Scholar
  66. Xu X, Liang T, Zhu J, Zheng D, Sun T (2019) Review of classical dimensionality reduction and sample selection methods for large-scale data processing. Neurocomputing 328:5–15Google Scholar
  67. Yildiz E, Sevim Y (2016) Comparison of linear dimensionality reduction methods on classification methods. In: 2016 National conference on electrical, electronics and biomedical engineering (ELECO), pp 161–164Google Scholar
  68. Zainuddin Z, Lai KH, Ong P (2016) An enhanced harmony search based algorithm for feature selection: applications in epileptic seizure detection and prediction. Comput Electr Eng 53:143–162Google Scholar
  69. ZaldVar D, PRez-Cisneros M, Cuevas E, Cienfuegos M, (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl Int J 40(16):6374–6384Google Scholar
  70. Zhang L, Lu X (2018) New fast feature selection methods based on multiple support vector data description. Appl Intell 48(7):1776–1790Google Scholar
  71. Zhang M, Xue B, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput J 18:261–276Google Scholar
  72. Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015:931256-1–931256-38MathSciNetzbMATHGoogle Scholar
  73. Zhu J, Wang Y, Feng L (2018) Novel artificial bee colony based feature selection method for filtering redundant information. Appl Intell 48:868–885Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Rehab Ali Ibrahim
    • 1
    • 2
  • Mohamed Abd Elaziz
    • 1
    • 2
  • Diego Oliva
    • 3
  • Erik Cuevas
    • 4
  • Songfeng Lu
    • 1
    • 5
    Email author
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt
  3. 3.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMexico
  4. 4.Departamento de ElectronicaUniversidad de Guadalajara, CUCEIGuadalajaraMexico
  5. 5.Shenzhen Huazhong University of Science and Technology Research InstituteShenzhenChina

Personalised recommendations