Skip to main content
SpringerLink
Log in

Enhancing Decision Combination in Classifier Committee via Positional Voting

  • Conference paper
  • First Online:
Computational Science – ICCS 2022 (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13351))

Included in the following conference series:

Abstract

In this work, we propose an approach for aggregating classifiers using positional voting techniques. We extend the positional voting by optimizing weights of the preferences to better aggregate the committee classifiers. Staring from initial weights determined by a voting algorithm the aggregating weights are optimized by a differential evolution algorithm. The algorithm has been evaluated on a human action dataset. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm achieves superior results against recent algorithms including skeleton-based ones.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)

    Book  Google Scholar 

  2. Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)

    Article  Google Scholar 

  3. Wozniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)

    Article  Google Scholar 

  4. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 3149–3157 (2017)

    Google Scholar 

  5. Sagi, O., Rokach, L.: Ensemble Learning: A Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8 (2018)

    Google Scholar 

  6. Osamor, V.C., Okezie, A.F.: Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis. Sci. Rep. 11(1), 14806 (2021)

    Article  Google Scholar 

  7. Bonab, H., Can, F.: Less is more: a comprehensive framework for the number of components of ensemble classifiers. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2735–2745 (2019)

    Article  MathSciNet  Google Scholar 

  8. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  9. van Erp, M., Vuurpijl, L., Schomaker, L.: An overview and comparison of voting methods for pattern recognition. In: Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 195–200 (2002)

    Google Scholar 

  10. Drotár, P., Gazda, M., Vokorokos, L.: Ensemble feature selection using election methods and ranker clustering. Inf. Sci. 480, 365–380 (2019)

    Article  MathSciNet  Google Scholar 

  11. Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020)

    Article  Google Scholar 

  12. Wang, L., Huynh, D.Q., Koniusz, P.: A comparative review of recent Kinect-based action recognition algorithms. IEEE Trans. Image Process. 29, 15–28 (2020)

    Article  MathSciNet  Google Scholar 

  13. Li, C., Huang, Q., Li, X., Wu, Q.: A multi-scale human action recognition method based on Laplacian pyramid depth motion images. In: Proceedings the 2nd ACM International Conference on Multimedia in Asia. ACM (2021)

    Google Scholar 

  14. Treliński, J., Kwolek, B.: Decision combination in classifier committee built on deep embedding features. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds.) ICCCI 2021. LNCS (LNAI), vol. 12876, pp. 480–493. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88081-1_36

    Chapter  Google Scholar 

  15. Treliński, J., Kwolek, B.: Human action recognition on raw depth maps. In: VCIP. IEEE (2021)

    Google Scholar 

  16. Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2495–2504 (2021)

    Google Scholar 

  17. Saari, D.G.: Basic Geometry of Voting. Springer, Cham (2015). https://doi.org/10.1007/978-3-642-57748-2

  18. Liu, M., Zhang, X., Chen, Z., Wang, X., Yang, T.: Fast stochastic AUC maximization with \(o(1/n)\)-convergence rate. In: Proceedings of the 35th International Conference on Machine Learning, PMLR, pp. 3189–3197 (2018)

    Google Scholar 

  19. Hu, J., Zheng, W., Lai, J., Zhang, J.: Jointly learning heterogeneous features for RGB-D activity recognition. In: CVPR, pp. 5344–5352 (2015)

    Google Scholar 

  20. Zhou, Z.H., Wu, J.X., Jiang, Y., Chen, S.F.: Genetic algorithm based selective neural network ensemble. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, vol. 2, pp. 797–802 (2001)

    Google Scholar 

  21. Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1109–1118. IEEE (2020)

    Google Scholar 

  22. Ke, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F., Boussaid, F.: Learning latent global network for skeleton-based action prediction. IEEE Trans. Img. Proc. 29, 959–970 (2020)

    Article  MathSciNet  Google Scholar 

  23. Hu, J., Zheng, W., Ma, L., Wang, G., Lai, J., Zhang, J.: Early action prediction by soft regression. IEEE Trans. PAMI 41(11), 2568–2583 (2019)

    Article  Google Scholar 

  24. Hu, J.-F., Zheng, W.-S., Ma, L., Wang, G., Lai, J.: Real-time RGB-D activity prediction by soft regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 280–296. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_17

    Chapter  Google Scholar 

  25. Wang, X., Hu, J.F., Lai, J.H., Zhang, J., Zheng, W.S.: Progressive teacher-student learning for early action prediction. In: CVPR, pp. 3551–3560 (2019)

    Google Scholar 

  26. Ren, Z., Zhang, Q., Gao, X., Hao, P., Cheng, J.: Multi-modality learning for human action recognition. Multimedia Tools Appl. 80(11), 16185–16203 (2020). https://doi.org/10.1007/s11042-019-08576-z

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by Polish National Science Center (NCN) under a research grant 2017/27/B/ST6/01743.

Author information

Authors and Affiliations

  1. AGH University of Science and Technology, 30 Mickiewicza, 30-059, Krakow, Poland

    Jacek Trelinski & Bogdan Kwolek

Authors
  1. Jacek Trelinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Kwolek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bogdan Kwolek .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trelinski, J., Kwolek, B. (2022). Enhancing Decision Combination in Classifier Committee via Positional Voting. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08754-7_64

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08753-0

  • Online ISBN: 978-3-031-08754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation