Advertisement

A Safe Semi-supervised Classification Algorithm Using Multiple Classifiers Ensemble

  • Jianhua ZhaoEmail author
  • Ning Liu
Article
  • 18 Downloads

Abstract

In order to improve the performance of semi-supervised learning, a safe semi-supervised classification algorithm using multiple classifiers ensemble (S3C-MC) is proposed. First, unlabeled samples are filtered and unlabeled samples with small ambiguity are selected for semi-supervised learning. Next, the labeled training set is sampled to multiple subsets and they generate multiple classifiers to predict the filtered unlabeled sample respectively. The predicted label is formed by multiple classifiers with weighted voting mechanism, and the weight of classifier is changing constantly according to the correctness of the prediction for unlabeled samples by classifier. Then, security verification is carried out to ensure that the classifier evolves in the direction of error reduction when the new sample is added. Only the label making classifiers error lower and having the same predictive value with the three classifiers in security verification is added into the labeled set to expand the number of labeled sets. Finally, the algorithm iterates until the unlabeled sample set is empty. The experiment is carried out on the UCI data set and the result shows that the proposed S3C-MC has good safety and has a higher classification rate.

Keywords

Semi-supervised learning Safety Multiple classifiers Ensemble Filter 

Notes

Acknowledgements

This work was supported by Shangluo Universities Key Disciplines Project, Discipline name: Mathematics; Natural Science Basic Research Plan in Shaanxi Province of China (No.2015JM6347); Science Research Plan of Shangluo University (No.14SKY026); Horizontal Project of Shangluo University (No.2018HXKY056, 19HKY082).

References

  1. 1.
    Li M, Li H, Zhou ZH (2009) Semi-supervised document retrieval. Inf Process Manage 45(3):341–355CrossRefGoogle Scholar
  2. 2.
    Silva NFFD, Coletta LFS, Hruschka ER (2016) A Survey and comparative study of tweet sentiment analysis via semi-supervised learning. ACM Comput Surv 49(1):1–26Google Scholar
  3. 3.
    Camps-Valls G, Munoz-Mari J, Gomez-Chova L et al (2009) Biophysical parameter estimation with a semisupervised support vector machine. IEEE Geosci Remote Sens Lett 6(2):248–252CrossRefGoogle Scholar
  4. 4.
    Dornaika F, El Traboulsi Y, Dornaika F, El TY (2015) Learning flexible graph-based semi-supervised embedding. IEEE Trans Cybern 46(1):206–218CrossRefGoogle Scholar
  5. 5.
    Peng Y, Zhai X, Zhao Y et al (2016) Semi-supervised cross-media feature learning with unified patch graph regularization. IEEE Trans Circuits Syst Video Technol 26(3):583–596CrossRefGoogle Scholar
  6. 6.
    Abdelgayed TS, Morsi WG, Sidhu TS (2018) Fault detection and classification based on co-training of semi-supervised machine learning. IEEE Trans Ind Electron 65(2):1595–1605CrossRefGoogle Scholar
  7. 7.
    Zhou ZH, Li M (2010) Semi-supervised learning by disagreement. Knowl Inf Syst 24(3):415–439MathSciNetCrossRefGoogle Scholar
  8. 8.
    Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory (COLT’98), pp 92–100. ACM, WisconsinGoogle Scholar
  9. 9.
    Yu ZW, Zhang YD, You JN et al (2019) Adaptive semi-supervised classifier ensemble for high dimensional data classification. IEEE Trans Cybern 49(2):366–379CrossRefGoogle Scholar
  10. 10.
    Keyvanpour MR, Imani MB (2013) Semi-supervised text categorization: exploiting unlabeled data using ensemble learning algorithms. Intell Data Anal 17(3):367–385CrossRefGoogle Scholar
  11. 11.
    Yu GX, Zhang GJ, Yu ZW et al (2012) Semi-supervised ensemble classification in subspaces. Appl Soft Comput 12(5):1511–1522CrossRefGoogle Scholar
  12. 12.
    Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541CrossRefGoogle Scholar
  13. 13.
    Li Y, Guo M (2012) A new relational Tri-training system with adaptive data editing for inductive logic programming. Knowl-Based Syst 35:73–185Google Scholar
  14. 14.
    Li YF, Liang DM (2019) Safe semi-supervised learning: a brief introduction. Front Comput Sci 4:669–676CrossRefGoogle Scholar
  15. 15.
    Li YF, Zhou ZH (2015) Towards making unlabeled data never hurt. IEEE Trans Pattern Anal Mach Intell 37(1):175–188CrossRefGoogle Scholar
  16. 16.
    Li YF, Zhou ZH (2011) Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI conference on artificial intelligence, pp 386–391Google Scholar
  17. 17.
    Sang N, Gan H, Fan Y et al (2019) Adaptive safety degree-based safe semi-supervised learning. Int J Mach Learn Cybernet 10:1101–1108CrossRefGoogle Scholar
  18. 18.
    Goldman S, Zhou Y (2000) Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th international conference on machine learning, pp 327–334Google Scholar
  19. 19.
    Soonthornphisaj N, Kijsirikul B (2004) Interative cross-training: an algorithm for learning from unlabeled Web pages. Int J Intell Syst 19(1–2):131–147CrossRefGoogle Scholar
  20. 20.
    Mallapragada PK, Jin R, Jain AK et al (2009) SemiBoost: boosting for semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 31(11):2000–2014CrossRefGoogle Scholar
  21. 21.
    Peng J, Aved AJ, Seetharaman G et al (2018) Multiview boosting with information propagation for classification. IEEE Trans Neural Netw Learn Syst 29(3):657–669MathSciNetCrossRefGoogle Scholar
  22. 22.
    Schwenk H, Bengio Y (2000) Boosting neural networks. Neural Comput 12(8):1869–1887CrossRefGoogle Scholar
  23. 23.
    Shen C, Li H (2009) On the dual formulation of boosting algorithms. IEEE Trans Pattern Anal Mach Intell 32(12):2216–2231CrossRefGoogle Scholar
  24. 24.
    Rashedi E, Mirzaei A (2013) A hierarchical cluster ensemble method based on boosting theory. Knowl-Based Syst 45(3):83–93CrossRefGoogle Scholar
  25. 25.
    Li J, Zhang L, Feng X, Jia K, Kong F (2019) Feature extraction and area identification of wireless channel in mobile communication. J Int Technol 20:544–553Google Scholar
  26. 26.
    Mi C, Shen Y, Mi WJ, Huang YF (2015) Ship identification algorithm based on 3D point cloud for automated ship loaders. J Coast Res 73:28–34CrossRefGoogle Scholar
  27. 27.
    Yang A, Li S, Ren C, Liu H, Han Y, Liu L (2018) Situational awareness system in the smart campus. IEEE Access 6:63976–63986CrossRefGoogle Scholar
  28. 28.
    Yang A, Li Y, Kong F, Wang G, Chen E (2018) security control redundancy allocation technology and security keys based on internet of things. IEEE Access. 6:50187–50196CrossRefGoogle Scholar
  29. 29.
    Yang Y, Zhong M, Yao H, Yu F, Fu X, Postolache O (2018) Internet of things for smart ports: technologies and challenges. IEEE Instrum Meas Mag 21:34–43CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Mathematics and Computer ApplicationShangluo UniversityShangluoChina
  2. 2.School of Economics ManagementShangluo UniversityShangluoChina

Personalised recommendations