Artificial Intelligence Review

, Volume 51, Issue 1, pp 19–32 | Cite as

Granular support vector machine: a review

  • Husheng Guo
  • Wenjian WangEmail author


The time complexity of traditional support vector machine (SVM) is \(O(l^{3})\) and l is the the training sample size, and it can not solve the large scale problems. Granular support vector machine (GSVM) is a novel machine learning model based on granular computing and statistical learning theory, and it can solve the low efficiency learning problem that exists in the traditional SVM and obtain satisfactory generalization performance, as well. This paper primarily reviews the past (rudiment), present (basic model) and future (development direction) of GSVM. Firstly, we briefly introduce the basic theory of SVM and GSVM. Secondly, we describe the past related research works conducted before the GSVM was proposed. Next, the latest thoughts, models, algorithms and applications of GSVM are described. Finally, we note the research and development prospects of GSVM.


Granular support vector machine Support vector machine Rudiment Basic model Prospects 



We would like to thank the anonymous reviewers for their valuable comments and suggestions. The work described in this paper was partially supported by the National Natural Science Foundation of China (Nos. 61503229, 61673249), Research Project Supported by Shanxi Scholarship Council of China (No. 2016-004), Natural Science Foundation of Shan Xi Province (No. 2015021096) and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 2015110).


  1. Asharaf S, Murty MN, Shevade SK (2007) Multiclass core vector machine. In: Proceedings of the 24th international conference on machine learning, Corvallis, OR, pp 41–48Google Scholar
  2. Bai XF, Wang WJ (2014) Saliency-SVM: an automatic approach for image segmentation. Neurocomputing 136(2014):243–255CrossRefGoogle Scholar
  3. Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16(2):320–330CrossRefGoogle Scholar
  4. Cao Y, Wan G, Wang F (2011) Predicting financial distress of Chinese listed companies using rough set theory and support vector machine. Asia-Pac J Oper Res 28(1):95–109MathSciNetCrossRefGoogle Scholar
  5. Chen B, Johnson M (2009) Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM). BMC Bioinform 10(11):296–300Google Scholar
  6. Cheng W, Zhang YP, Zhao S (2009) Research of yield prediction model based on support vector machine within the framework of quotient space theory. J China Agric Univ 14(5):135–139Google Scholar
  7. Deb AK, Jayadeva, Gopal M (2007) SVM-based tree-type neural networks as a critic in adaptive critic designs for control. IEEE Trans Neural Netw 18(4):1016–1030CrossRefGoogle Scholar
  8. Ding SF, Huang HJ, Yu JZ et al (2015) Research on the hybrid models of granular computing and support vector machine. Artif Intell Rev 43(6):565–577CrossRefGoogle Scholar
  9. D’Urso P, Leski JM (2016) Fuzzy c-ordered medoids clustering for interval-valued data. Pattern Recognit 58:49–67CrossRefGoogle Scholar
  10. Erfani SM, Rajasegarar S, Karunasekera S et al (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit 58:121–134CrossRefGoogle Scholar
  11. Guo G, Zhang JS (2007) Reducing examples to accelerate support vector regression. Pattern Recognit Lett 28(16):2173–2183CrossRefGoogle Scholar
  12. Guo HS, Wang WJ, Men CQ (2009) A novel learning model-Kernel Granular support vector machine. In: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, Baoding, China, pp 930–935Google Scholar
  13. Guo HS, Wang WJ (2013) Dynamical granular support vector regression machine. J Softw 24(11):2535–2547MathSciNetCrossRefzbMATHGoogle Scholar
  14. Guo HS, Wang WJ (2015) An active learning-based SVM multiple classification model. Pattern Recognit 48(5):1577–1597CrossRefzbMATHGoogle Scholar
  15. Guo HS, Wang WJ (2016) Support vector machine based on hierarchical and dynamical granulation. Neurocomputing 211:22–33CrossRefGoogle Scholar
  16. Hsu CW, Lin CJ (2013) LIBSVM software package.
  17. Huang CH, Kao HY (2009) Interval regression analysis with soft-margin reduced support vector machine. Lecture Notes in Computer Science, Next-Generation Applied Intelligence 5579:826–835CrossRefGoogle Scholar
  18. Joachims T (1999) Making large-scale SVM learning practical. In: Schölkopf B, Burges C, Smola A (eds) Advances in kernel methods-support vector learning. MIT Press, Cambridge, pp 169–184Google Scholar
  19. Katagiri S, Abe S (2006) Incremental training of support vector machines using hyperspheres. Pattern Recognit Lett 27(13):1495–1507CrossRefGoogle Scholar
  20. Kumar MA, Gopal M (2010) A hybrid SVM based decision tree. Pattern Recognit 43(12):3977–3987CrossRefzbMATHGoogle Scholar
  21. Kumar P, Jayaraman VK, Kulkarni BD (2007) Granular support vector machine based method for prediction of solubility of proteins on overexpression in Escherichia coli. Lecture Notes in Computer Science, Pattern Recognition and Machine Intelligence 4815:406–415CrossRefGoogle Scholar
  22. Lin KP, Pai PF (2010) A fuzzy support vector regression model for business cycle predictions. Expert Syst Appl 37(7):5430–5435CrossRefGoogle Scholar
  23. Niu XX, Ching YS (2012) A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognit 45(4):1318–1325CrossRefGoogle Scholar
  24. Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Puerto Rico, pp 130–136Google Scholar
  25. Pang SN, Kasabov N (2008) R-SVMT: discovering the knowledge of association rule over SVM classification trees. In: Proceedings of the international joint conference on neural networks, pp 2486–2493Google Scholar
  26. Pereira F, Gordon G (2006) The support vector decomposition machine. In: Proceedings of the 23rd international conference on machine learning, Pittsburgh, PAGoogle Scholar
  27. Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges C, Smola A (eds) Advances in kernel methods-support vector learning. MIT Press, CambridgeGoogle Scholar
  28. Ruan JH, Wang XP, Shi Y (2013) Developing fast predictors for large-scale time series using fuzzy granular support vector machines. Appl Soft Comput 13(9):3981–4000CrossRefGoogle Scholar
  29. Ruan JH, Shi Y (2016a) Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Inf Sci 373:557–570CrossRefGoogle Scholar
  30. Ruan JH, Wang XP, Chan FTS et al (2016b) Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. Int J Prod Res 54(14):1–19CrossRefGoogle Scholar
  31. Shih PC, Liu CJ (2006) Face detection using discriminating feature analysis and support vector machine. Pattern Recognit 39(2):260–276CrossRefGoogle Scholar
  32. Tang YC, Jin B, Sun Y et al (2004) Granular support vector machines for medical binary classification problems. In: Proceedings of the IEEE CIBIB. IEEE Computational Intelligence Society, Piscataway, HJ, pp 73–78Google Scholar
  33. Tang YC, Jin B, Zhang YQ (2005) Granular support vector machines with association rules mining for protein homology prediction. Artif Intell Med 35:121–134CrossRefGoogle Scholar
  34. Tang YC, Krasser S, Judge P et al (2006) Fast and effective spam sender detection with granular SVM on highly imbalanced mail server behavior data. In: Proceedings of 2nd international conference on collaborative computing: networking, applications and worksharing (CollaborateCom), Atlanta, Georgia, USAGoogle Scholar
  35. Tang YC, Zhang YQ, Chawla NV et al (2009) SVMs modeling for highly for highly imbalanced classification. IEEE Trans Syst Man Cybern 39(1):281–288CrossRefGoogle Scholar
  36. Teng XY, Yuan J, Yu HY (2009) Probability density estimation based on SVM. In: Proceedings of the global mobile congress. IEEE, Shanghai, China, pp 1–4Google Scholar
  37. Tian YJ, Qi ZQ (2014) Review on: twin support vector machines. Ann Data Sci 1(2):253–277CrossRefGoogle Scholar
  38. Tomar D, Agarwal S (2015) Twin support vector machine: a review from 2007 to 2014. Egypt Inf J 16(1):55–69CrossRefGoogle Scholar
  39. Tsang IW, Kwok JT, Cheung PM (2005) Core vector machines: fast SVM training on very large data sets. J Mach Learn Res 6:363–392MathSciNetzbMATHGoogle Scholar
  40. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefzbMATHGoogle Scholar
  41. Wang WJ, Men CQ (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558CrossRefGoogle Scholar
  42. Wang WJ, Guo HS, Jia YF et al (2013) Granular support vector machine based on mixed measure. Neurocomputing 101:116–128CrossRefGoogle Scholar
  43. Xu H, Lemischka IR, Ma’Ayan A (2010) SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst Biol 4(1):3395–3407CrossRefGoogle Scholar
  44. Yang MH, Abup N (2000) A geometric approach to train support vector machines. In: Proceedings of IEEE conference on computer vision and pattern recognition, Hilton Head Island, South Carolina, USA, pp 430–437Google Scholar
  45. Yao JT (2007) A ten year review of granular computing. In: Proceedings of 2007 IEEE international conference on granular computing, Silicon Valley, USA, pp 734–739Google Scholar
  46. Yu H, Yang J, Han JW et al (2005) Making SVMs scalable to large data sets using hierarchical cluster indexing. Data Min Knowl Discov 11(3):295–321MathSciNetCrossRefGoogle Scholar
  47. Yuan Y (2009) Research and application of minimum enclosing ball SVM algorithm. Nanjing University of Aeronautics and Astronautics, NanjingGoogle Scholar
  48. Zhang XG (1999) Using class-center vectors to build support vector machines. In: Proceedings of the IEEE conference on neural networks for signal processing, Wisconsin, USA, pp 3–11Google Scholar
  49. Zhong C, Pedrycz W, Wang D et al (2016) Granular data imputation: a framework of granular computing. Appl Soft Comput 46:307–316CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
  2. 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of EducationShanxi UniversityTaiyuanChina

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