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Transfer synthetic over-sampling for class-imbalance learning with limited minority class data

  • Xu-Ying LiuEmail author
  • Sheng-Tao Wang
  • Min-Ling Zhang
Research Article
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Abstract

The problem of limited minority class data is encountered in many class imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Most sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.

Keywords

machine learning data mining class imbalance over sampling boosting transfer learning 

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Notes

Acknowledgements

The authors wish to thank the associate editor and anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Key R&D Program of China (2017YFB1002801), the National Natural Science Foundation of China (Grant Nos. 61473087, 61573104), the Natural Science Foundation of Jiangsu Province (BK20141340), and partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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References

  1. 1.
    He H, Garcia E A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263–1284CrossRefGoogle Scholar
  2. 2.
    Liu X Y, Wu J, Zhou Z H. Exploratory undersampling for classimbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(2): 539–550CrossRefGoogle Scholar
  3. 3.
    Cieslak D, Chawla N. Learning decision trees for unbalanced data. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2008, 241–256CrossRefGoogle Scholar
  4. 4.
    Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(4): 463–484CrossRefGoogle Scholar
  5. 5.
    Wang S, Minku L L, Yao X. Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1356–1368CrossRefGoogle Scholar
  6. 6.
    Yan Y, Chen M, Shyu M L, Chen S C. Deep learning for imbalanced multimedia data classification. In: Proceedings of the 2015 IEEE International Symposium on Multimedia. 2015, 483–488Google Scholar
  7. 7.
    Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy P J. Training deep neural networks on imbalanced data sets. In: Proceedings of the 2016 International Joint Conference on Neural Networks. 2016, 4368–4374CrossRefGoogle Scholar
  8. 8.
    Fawcett T, Provost F J. Combining data mining and machine learning for effective user profiling. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 1996, 8–13Google Scholar
  9. 9.
    Kubat M, Holte R C, Matwin S. Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 1998, 30(2–3): 195–215CrossRefGoogle Scholar
  10. 10.
    Lewis D D, Ringuette M. A comparison of two learning algorithms for text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval. 1994, 81–93Google Scholar
  11. 11.
    Wang S, Yao X. Using class imbalance learning for software defect prediction. IEEE Transactions on Reliability, 2013, 62(2): 434–443CrossRefGoogle Scholar
  12. 12.
    Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(6): 1145–1159CrossRefGoogle Scholar
  13. 13.
    Yang Q, Wu X. 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making, 2006, 5(4): 597–604CrossRefGoogle Scholar
  14. 14.
    Weiss G M. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 7–19CrossRefGoogle Scholar
  15. 15.
    Weiss G M. Mining with Rare Cases. Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA. 2005, 765–776CrossRefGoogle Scholar
  16. 16.
    Khoshgoftaar T M, Seiffert C, Hulse J V, Napolitano A, Folleco A. Learning with limited minority class data. In: Proceedings of the 6th International Conference on Machine Learning and Applications. 2007, 348–353Google Scholar
  17. 17.
    Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16: 321–357CrossRefzbMATHGoogle Scholar
  18. 18.
    Han H, Wang W Y, Mao B H. Borderline-SMOTE: a new oversampling method in imbalanced data sets learning. In: Proceedings of the International Conference on Intelligent Computing. 2005, 878–887Google Scholar
  19. 19.
    Batista G E, Prati R C, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM SGKDD Explorations Newsletter, 2004, 6(1): 20–29CrossRefGoogle Scholar
  20. 20.
    Laurikkala J. Improving identification of difficult small classes by balancing class distribution. In: Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe. 2001, 63–66CrossRefGoogle Scholar
  21. 21.
    He H, Bai Y, Garcia E A, Li S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks. 2008, 1322–1328Google Scholar
  22. 22.
    Das B, Krishnan N C, Cook D J. wRACOG: a gibbs sampling-based oversampling technique. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2013, 111–120Google Scholar
  23. 23.
    Zhang H, Li M. RWO-sampling: a random walk over-sampling approach to imbalanced data classification. Information Fusion, 2014, 20: 99–116CrossRefGoogle Scholar
  24. 24.
    Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345–1359CrossRefGoogle Scholar
  25. 25.
    Galar M, Fernández A, Barrenechea E, Herrera F. EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition, 2013, 46(12): 3460–3471CrossRefGoogle Scholar
  26. 26.
    Ramentol E, Caballero Y, Bello R, Herrera F. SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. Knowledge and Information Systems, 2012, 33(2): 245–265CrossRefGoogle Scholar
  27. 27.
    Wang S, Yao X. Multiclass imbalance problems: analysis and potential solutions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(4): 1119–1130CrossRefGoogle Scholar
  28. 28.
    Liu X Y, Li Q Q. Learning from combination of data chunks for multiclass imbalanced data. In: Proceedings of the 2014 International Joint Conference on Neural Networks. 2014, 1680–1687CrossRefGoogle Scholar
  29. 29.
    Li S, Wang Z, Zhou G, Lee S Y M. Semi-supervised learning for imbalanced sentiment classification. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence. 2011, 1826–1832Google Scholar
  30. 30.
    Zhang M L, Li Y K, Liu X Y. Towards class-imbalance aware multilabel learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015, 4041–4047Google Scholar
  31. 31.
    Hoens T R, Chawla N V. Learning in non-stationary environments with class imbalance. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 168–176Google Scholar
  32. 32.
    Wang S, Minku L L, Yao X. Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1356–1368CrossRefGoogle Scholar
  33. 33.
    Cao H, Li X L, Woon Y K, Ng S K. SPO: structure preserving oversampling for imbalanced time series classification. In: Proceeding of the 11st IEEE International Conference on Data Mining. 2011, 1008–1013CrossRefGoogle Scholar
  34. 34.
    Cao H, Li X L, Woon D Y K, Ng S K. Integrated oversampling for imbalanced time series classification. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2809–2822CrossRefGoogle Scholar
  35. 35.
    Chawla N V, Lazarevic A, Hall L O, Bowyer K W. SMOTEBoost: improving prediction of the minority class in boosting. In: Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases. 2003, 107–119Google Scholar
  36. 36.
    Wang S, Yao X. Diversity analysis on imbalanced data sets by using ensemble models. In: Proceedings of IEEE Symposium on Computational Intelligence and Data Mining. 2009, 324–331Google Scholar
  37. 37.
    Sun Y, Kamel M S, Wong A K, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition, 2007, 40(12): 3358–3378CrossRefzbMATHGoogle Scholar
  38. 38.
    Seiffert C, Khoshgoftaar T M, Van Hulse J, Napolitano A. RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A (Systems and Humans), 2010, 40(1): 185–197CrossRefGoogle Scholar
  39. 39.
    Tomek I. Two modifications of CNN. IEEE Transactions of System Man Cybernetics, 1976, 6: 769–772MathSciNetzbMATHGoogle Scholar
  40. 40.
    Raina R, Battle A, Lee H, Packer B, Ng A Y. Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 759–766Google Scholar
  41. 41.
    Wei Y, Zhu Y, Leung C W, Song Y, Yang Q. Instilling social to physical: co-regularized heterogeneous transfer learning. In: Proceedings of the 13rd AAAI Conference on Artificial Intelligence. 2016, 1338–1344Google Scholar
  42. 42.
    Weiss K, Khoshgoftaar T M, Wang D. A survey of transfer learning. Journal of Big Data, 2016, 3(1): 1–40CrossRefGoogle Scholar
  43. 43.
    Al-Stouhi S, Reddy C K. Transfer learning for class imbalance problems with inadequate data. Knowledge and Information Systems, 2016, 48(1): 201–208CrossRefGoogle Scholar
  44. 44.
    Ge L, Gao J, Ngo H, Li K, Zhang A. On handling negative transfer and imbalanced distributions in multiple source transfer learning. Statistical Analysis and Data Mining, 2014, 7(4): 254–271MathSciNetCrossRefGoogle Scholar
  45. 45.
    Dai W, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 193–200Google Scholar
  46. 46.
    Blake C, Keogh E, Merz C J. UCI repository of machine learning databases. University of California, Irvine, CA, 1996Google Scholar
  47. 47.
    Breiman L, Friedman J, Olshen R A, Stone C J. Classification and Regression Trees. London: Routledge Press, 2017CrossRefzbMATHGoogle Scholar
  48. 48.
    Schapire R E. A brief introduction to Boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence. 1999, 1401–1406Google Scholar
  49. 49.
    Barandela R, Valdovinos R M, Snchez J S. New applications of ensembles of classifiers. Pattern Analysis and Applications, 2003, 6(3): 245–256MathSciNetCrossRefGoogle Scholar
  50. 50.
    Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123–140zbMATHGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xu-Ying Liu
    • 1
    • 2
    • 3
    Email author
  • Sheng-Tao Wang
    • 1
    • 2
    • 3
  • Min-Ling Zhang
    • 1
    • 2
    • 3
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Computer Network and Information Integration (Southeast University)Ministry of EducationNanjingChina
  3. 3.Collaborative Innovation Center for Wireless Communications TechnologyNanjingChina

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