Skip to main content

Advertisement

Springer Nature Link
Log in

A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

In order to improve the generalization ability and recognition efficiency of the maritime surveillance radar, a novel selection ensemble technique, termed KMRRC, based on k-medoids clustering and random reference classifier (RRC) is proposed. By disturbing the training set base classifiers are generated, which are then divided into several clusters based on pairwise diversity metrics, finally the RRC model is used to select several most competent classifiers from each cluster to classify each query object. The performance of KMRRC is compared against nine ensemble learning methods using a self-built high range resolution profile (HRRP) data set and twenty UCI databases. The experimental results clearly show the KMMRRC’s feasibility and effectiveness. In addition, the influence of the selection of diversity measures is studied concurrently.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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.

Institutional subscriptions

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Kittler J, Hatef M, Duin R P W, Matas J (1998) On combining classifiers. IEEE T Pattern Anal 20(3):226–239

    Article  Google Scholar 

  2. Wang XZ, Xing HJ, Li Y et al.(2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE T Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  3. Ludmila IK (2014) Combining pattern classifiers: methods and algorithms, 2nd edn. Wiley, New York, pp 178–183

    MATH  Google Scholar 

  4. Woźniak M, Graña M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16(1):3–17

    Article  Google Scholar 

  5. Kuncheva LI (2002) Switching between selection and fusion in combining classifiers: an experiment. IEEE T Syst Man Cybern 32(2):146–156

    Article  Google Scholar 

  6. Azizi N, Farah N, Ennaji A (2012) New dynamic classifiers selection approach for handwritten recognition. In: Proceedings of international conference on artificial neural networks and machine learning, Springer, pp 189–196

  7. Mousavi R, Eftekhari M (2015) A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches. Appl Soft Comput 37(C):652–666

    Article  Google Scholar 

  8. Britto AS, Sabourin R, Oliveira L E S (2014) Dynamic selection of classifiers—a comprehensive review. Pattern Recogn 47(11):3665–3680

    Article  Google Scholar 

  9. Ko AH, Sabourin R, Britto AS (2008) From dynamic classifier selection to dynamic ensemble selection. Pattern Recogn 41(5):1735–1748

    Article  Google Scholar 

  10. Cavalin PR, Sabourin R, Suen CY (2013) Dynamic selection approaches for multiple classifier systems. Neural Comput Appl 22(3–4):673–688

    Article  Google Scholar 

  11. Cruz R M O, Cavalcanti G D C, Ren TI (2011) A method for dynamic ensemble selection based on a filter and an adaptive distance to improve the quality of the regions of competence. In: Proceedings of International Joint Conference on Neural Networks, IEEE, pp 1126–1133

  12. Azizi N, Farah N, Khadir T (2015) Multi stage dynamic ensemble selection using heterogeneous learning algorithms: application on classification problems. Int J Knowl Manag Stud 142(1):98–104

    Google Scholar 

  13. Santos E M D, Sabourin R, Maupin P (2008) A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recogn 41(10):2993–3009

    Article  Google Scholar 

  14. Giacinto G, Roli F (2001) Dynamic classifier selection based on multiple classifier behavior. Pattern Recogn 34(9):1879–1881

    Article  Google Scholar 

  15. Krijthe JH, Ho TK, Loog M (2012) Improving cross-validation based classifier selection using meta-learning. In: Proceedings of international conference on pattern recognition, IEEE, pp 2873–2876

  16. Cruz RM, Sabourin R, Cavalcanti GDC et al.(2015) META-DES: A dynamic ensemble selection framework using meta-learning. Pattern Recogn 48(5):1925–1935

    Article  Google Scholar 

  17. Woloszynski T, Kurzynski M (2011) A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn 44(10–11):2656–2668

    Article  Google Scholar 

  18. Huang D, Lai JH, Wang CD (2016) Ensemble clustering using factor graph. Pattern Recognition Pattern Recogn 50:131–142

    Article  Google Scholar 

  19. Huang D, Lai JH, Wang CD (2016) Robust ensemble clustering using probability trajectories. IEEE T Knowl Data Eng 28(5):1312–1326

    Article  Google Scholar 

  20. Zenobi G, Cunningham P (2001) Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error. In: Proceedings of European conference on machine learning, Springe, pp 576–587

  21. Canuto AMP, Abreu MCC, Oliveira LDM et al.(2007) Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recogn Lett 28(4):472–486

    Article  Google Scholar 

  22. Woloszynski T, Kurzynski M, Podsiadlo P et al.(2012) A measure of competence based on random classification for dynamic ensemble selection. Inf Fusion 13(3):207–213

    Article  Google Scholar 

  23. Cameron WL, Leung LK (1990) Feature motivated polarization scattering matrix decomposition. In: Proceedings of international conference on radar, IEEE, pp 549–557

  24. Chen Y, Jiang H, Li C, Jia X (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE T Geosci Rem 54(10):1–20

    Article  Google Scholar 

  25. Pilcher CM, Khotanzad A (2011) Maritime ATR using classifier combination and high resolution range profiles. IEEE Trans Aerosp Electron Syst 47(4):2558–2573

    Article  Google Scholar 

  26. Cloude SR, Pottier E (1997) An entropy based classification scheme for land applications of polarimetric SAR. IEEE T Geosci Remote 35(1):68–78

    Article  Google Scholar 

  27. Berizzi F, Martorella M, Capria A et al.(2008) H/α polarimetric features for man-made target classification. In: Proceedings of international conference on radar conference, IEEE, pp 1–6

  28. Lei L, Wang XD, Xing YQ et al.(2013) Multi-polarized HRRP classification by SVM and DS evidence theory. Control Decis 28(6):861–866 (Chinese)

    MATH  Google Scholar 

  29. Guo L (2009) Wideband radar target polarimetric feature extraction and recognition method based on kernel method. Dissertation, National University of Defense Technology. (in chinese)

  30. Fan XM, Hu SL, He JB (2016) Feature extraction and selection of Full Polarization HRRP in the target recognition process of maritime surveillance radar. J Electrs Inf Technol 38(12):3261–3268

    Google Scholar 

  31. Yeung DS, Wang XZ (2002) Improving performance of similarity-based clustering by feature weight learning. IEEE T Pattern Anal 24(4):556–561

    Article  Google Scholar 

  32. Tang EK, Suganthan PN, Yao X (2006) An analysis of diversity measures. Mach Learn 65(1):247–271

    Article  Google Scholar 

  33. Balabantaray RC, Sarma C, Jha M (2015) Document clustering using K-means and K-medoids. Int J Knowl Based Comput Syst 1:7–13

    Google Scholar 

  34. Azizi N, Farah N (2012) From static to dynamic ensemble of classifiers selection: application to Arabic handwritten recognition. IOS press, USA, pp 279–288

    Google Scholar 

  35. Demsar J (2015) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1–30

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by Grant No. (61401493) from the National Natural Science Foundation of China and No.(9140A01010415JB11002) from the National Ministries Foundation of China.

Author information

Authors and Affiliations

  1. Electronics Engineering College, Naval University of Engineering, Wuhan, 430033, China

    Xueman Fan, Shengliang Hu & Jingbo He

Authors
  1. Xueman Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shengliang Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jingbo He
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xueman Fan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, X., Hu, S. & He, J. A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier. Int. J. Mach. Learn. & Cyber. 10, 515–525 (2019). https://doi.org/10.1007/s13042-017-0732-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-017-0732-2

Keywords