Skip to main content
SpringerLink
Log in

A Boosting-Based Decision Fusion Method for Learning from Large, Imbalanced Face Data Set

  • Chapter
  • First Online:
Quantum Computing:An Environment for Intelligent Large Scale Real Application

Part of the book series: Studies in Big Data ((SBD,volume 33))

Abstract

The acquisition of face images is usually limited due to policy and economy considerations, and hence the number of training examples of each subject varies greatly. The problem of face recognition with imbalanced training data has drawn attention of researchers and it is desirable to understand in what circumstances imbalanced data set affects the learning outcomes, and robust methods are needed to maximize the information embedded in the training data set without relying much on user introduced bias. In this article, we study the effects of uneven number of training images for automatic face recognition and proposed a boosting-based decision fusion method that suppresses the face recognition errors by training an ensemble with subsets of examples. By recovering the balance among classes in the subsets, our proposed multiBoost.imb method circumvents the class skewness and demonstrates improved performance. Experiments are conducted with four popular face data sets and two synthetic data sets. The results of our method exhibits superior performance in high imbalanced scenarios compared to AdaBoost.M1, SAMME, RUSboost, SMOTEboost, SAMME with SMOTE sampling and SAMME with random undersampling. Another advantage that comes with using subsets of examples is the significant gain in efficiency.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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. Liu, Y.-H., Chen, Y.-T.: Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Trans. Neural Netw. 18(1), 178–192 (2007)

    Article  Google Scholar 

  2. He, H., Edwardo, G.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Google Scholar 

  3. Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)

    Google Scholar 

  4. Zhang, Y., Zhou, Z.-H.: Cost-sensitive face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1758–1769 (2010)

    Article  Google Scholar 

  5. Lu, J., Tan, Y.-P.: A doubly weighted approach for appearance-based subspace learning methods. IEEE Trans. Inf. Forensic Secur. 5(1), 71–78 (2010)

    Article  MathSciNet  Google Scholar 

  6. Liu, Y.-H., Chen, Y.-T., Lu, S.-S.: Face detection using kernel pca and imbalanced svm. In: Lecture Notes in Computer Science, International Conference on Natural Computation, vol. 4221, pp. 351–360 (2006)

    Google Scholar 

  7. Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2000)

    MathSciNet  MATH  Google Scholar 

  8. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  9. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning, pp. 80–91 (1999)

    Google Scholar 

  10. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)

    MATH  Google Scholar 

  11. Schapire, R.E.: Using output codes to boost multi-class learning problems. In: Proceedings of the 14th International Conference on Machine Learning, pp. 313–321 (1997)

    Google Scholar 

  12. Guruswami, V., Sahai, A.: Multiclass learning, boosting, and error-correcting codes. In: Proceedings of the 12th Annual Conference on Computational Learning Theory, pp. 145–155 (1999)

    Google Scholar 

  13. Zhu, J., Zou, H., Rosset, S., Hastie, T.: Multi-class adaboost. Stat. Interface 2, 349–360 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Mukherjee, I., Schapire, R.E.: A theory of multiclass boosting. In: Proceedings of Twenty-Fourth Annual Conference on Neural Information Processing Systems (2010)

    Google Scholar 

  15. Karakoulas, G., Shawe-Taylor, J.: Optimizing classifiers for imbalanced training sets. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, pp. 253–259, Cambridge, MA, USA. MIT Press (1999)

    Google Scholar 

  16. Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40, 3358–3378 (2007)

    Google Scholar 

  17. Wang, B.X., Japkowicz, N.: Boosting support vector machines for imbalanced data sets. In: Foundations of Intelligent Systems, pp. 38–47 (2008)

    Google Scholar 

  18. Fan, W., Stolfo, S.J., Zhang, J., Chan, P.K.: Adacost: misclassification cost-sensitive boosting. In: 16th International Conference on Machine Learning (1999)

    Google Scholar 

  19. Joshi, M.V., Kumar, V., Agarwal, R.C.: Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: First IEEE International Conference on Data Mining, pp. 257–264 (2001)

    Google Scholar 

  20. Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer. K.W.: Smoteboost: improving prediction of the minority. In: Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 107–119 (2003)

    Google Scholar 

  21. Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor. 6(1), 30–39 (2004)

    Google Scholar 

  22. Geiler, O.J., Hong, L., Yue-Jian, G.: An adaptive sampling ensemble classifier for learning from imbalanced data sets. In: International MultiConference of Engineers and Computer Scientists, vol. 1, March 2010

    Google Scholar 

  23. Chen, S., He, H., Garcia, E.A.: RAMOBoost: ranked minority oversampling in boosting. IEEE Trans. Neural Netw. 21(10), 1624–1642 (2010)

    Article  Google Scholar 

  24. Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man. Cybern. Part A Syst. Hum. 40(1), 185–197 (2010)

    Article  Google Scholar 

  25. Galar, M., Fernandez, A., Barrenechea, E., Francisco, H.: EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn. 46(12), 3460–3471 (2013)

    Google Scholar 

  26. Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., Li, S.Z.: Ensemble-based discriminant learning with boosting for face recognition. IEEE Trans. Neural Netw. 17(1), 166–178 (2006)

    Article  Google Scholar 

  27. Eibl, G., Pheiffer, K.-P.: Multiclass boosting for weak classifiers. J. Mach. Learn. Res. 6, 189–210 (2005)

    Google Scholar 

  28. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  29. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  30. Huang, G.B., Mattar, M., Lee, H., Learned-Miller, E.: Learning to align from scratch. In: Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, United States, December 3–6, 2012

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA

    Xiaohui Yuan & Mohamed Abouelenien

  2. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

Authors
  1. Xiaohui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Abouelenien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Elhoseny
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaohui Yuan .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University , Cairo, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

  3. Systems Research Institute, Polish Academy of Sciences , Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Yuan, X., Abouelenien, M., Elhoseny, M. (2018). A Boosting-Based Decision Fusion Method for Learning from Large, Imbalanced Face Data Set. In: Hassanien, A., Elhoseny, M., Kacprzyk, J. (eds) Quantum Computing:An Environment for Intelligent Large Scale Real Application . Studies in Big Data, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-63639-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63639-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63638-2

  • Online ISBN: 978-3-319-63639-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation