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Improving ADABoost Algorithm with Weighted SVM for Imbalanced Data Classification

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Future Data and Security Engineering (FDSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13076))

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Abstract

Recently, different boosting algorithms have been proposed in order to improve the performance of classification for imbalanced data. In this paper, we present an improved ADABoost algorithm, called Im.ADABoost, for imbalanced data including two main improvements: (i) initializing different error weights adapted to the imbalance rate of the datasets; (ii) calculating the confidence weights of the member classifier that is sensitive to the total errors caused on the positive label. Additionally, we combine Im.ADABoost with Weighted-SVM to enhance classification efficiency on imbalanced datasets. Our experimental results show some promising potential of the proposed algorithm.

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Notes

  1. 1.

    https://www.kaggle.com/datasets.

References

  1. Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_7

    Chapter  Google Scholar 

  2. Benjamin, X.W., Nathalie, J.: Boosting support vector machines for imbalanced data sets. Knowl. Inf. Syst. 21, 1–20 (2010). https://doi.org/10.1007/s10115-009-0198-y

    Article  Google Scholar 

  3. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  4. Dong, X., Gao, H., Guo, L., Li, K., Duan, A.: Deep cost adaptive convolutional network: a classification method for imbalanced mechanical data. IEEE Access 8, 71486–71496 (2020). https://doi.org/10.1109/ACCESS.2020.2986419

    Article  Google Scholar 

  5. Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the Seventeenth International Conference on Artificial Intelligence: 4–10 August 2001, Seattle, vol. 1, pp. 973–978 (2001)

    Google Scholar 

  6. Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256–285 (1995). https://doi.org/10.1006/inco.1995.1136

    Article  MathSciNet  MATH  Google Scholar 

  7. 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  Google Scholar 

  8. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: Ordering-based pruning for improving the performance of ensembles of classifiers in the framework of imbalanced datasets. Inf. Sci. 354, 178–196 (2016). https://doi.org/10.1016/j.ins.2016.02.056

    Article  Google Scholar 

  9. Guo, H., Viktor, H.: Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor. 6(1), 30–39 (2004). https://doi.org/10.1145/1007730.1007736

    Article  Google Scholar 

  10. Hilario, A., Garcia Lopez, S., Galar, M., Prati, R., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets, Artificial Intelligence. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98074-4_9

    Book  Google Scholar 

  11. Johnson, J., Khoshgoftaar, T.: Survey on deep learning with class imbalance. J. Big Data 6, 1–54 (2019). https://doi.org/10.1186/s40537-019-0192-5

    Article  Google Scholar 

  12. Jordan, M., Mitchell, T.: Machine learning: trends, perspectives, and prospects. Science (New York N.Y.) 349, 255–60 (2015). https://doi.org/10.1126/science.aaa8415

    Article  MathSciNet  MATH  Google Scholar 

  13. Khang, T.D., Tran, M.K., Fowler, M.: A novel semi-supervised fuzzy c-means clustering algorithm using multiple fuzzification coefficients. Algorithms 14(9), 258 (2021)

    Article  Google Scholar 

  14. Khang, T.D., Vuong, N.D., Tran, M.K., Fowler, M.: Fuzzy c-means clustering algorithm with multiple fuzzification coefficients. Algorithms 13(7), 1–11 (2020)

    Article  MathSciNet  Google Scholar 

  15. Lee, W., Jun, C.H., Lee, J.S.: Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification. Inf. Sci. 381, 92–103 (2016). https://doi.org/10.1016/j.ins.2016.11.014

    Article  Google Scholar 

  16. Li, X., Wang, L., Sung, E.: AdaBoost with SVM-based component classifiers. Eng. Appl. Artif. Intell. 21, 785–795 (2008). https://doi.org/10.1016/j.engappai.2007.07.001

    Article  Google Scholar 

  17. Lima, N.H.C., Neto, A.D.D., Dantas de Melo, J.: Creating an ensemble of diverse support vector machines using AdaBoost. In: 2009 International Joint Conference on Neural Networks, pp. 1802–1806 (2009)

    Google Scholar 

  18. Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Netw. 13(2), 464–471 (2002). https://doi.org/10.1109/72.991432

    Article  Google Scholar 

  19. Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009). https://doi.org/10.1109/TSMCB.2008.2007853

    Article  Google Scholar 

  20. Rengasamy, S., Punniyamoorthy, M.: Performance enhanced boosted SVM for imbalanced datasets. Appl. Soft Comput. 83, 105601 (2019). https://doi.org/10.1016/j.asoc.2019.105601

    Article  Google Scholar 

  21. Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)

    Article  Google Scholar 

  22. Tao, X., et al.: Affinity and class probability-based fuzzy support vector machine for imbalanced data sets. Neural Netw. 122, 289–307 (2020)

    Article  Google Scholar 

  23. Tharwat, A., Gabel, T.: Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput. Appl. 32(11), 6925–6938 (2019). https://doi.org/10.1007/s00521-019-04159-z

    Article  Google Scholar 

  24. Turki, T., Wei, Z.: Boosting support vector machines for cancer discrimination tasks. Comput. Biol. Med. 101, 236–249 (2018). https://doi.org/10.1016/j.compbiomed.2018.08.006

    Article  Google Scholar 

  25. Yan, Y., Chen, M., Shyu, M.L., Chen, S.C.: Deep learning for imbalanced multimedia data classification. In: 2015 IEEE International Symposium on Multimedia (ISM), pp. 483–488. IEEE, Miami (2015). https://doi.org/10.1109/ISM.2015.126

  26. Zeng, M., Zou, B., Wei, F., Liu, X., Wang, L.: Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data. In: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), pp. 225–228 (2016). https://doi.org/10.1109/ICOACS.2016.7563084

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Author information

Authors and Affiliations

  1. Hanoi University of Science and Technology, Hanoi, Vietnam

    Vo Duc Quang, Tran Dinh Khang & Nguyen Minh Huy

  2. Vinh University, Vinh City, Vietnam

    Vo Duc Quang

Authors
  1. Vo Duc Quang
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  2. Tran Dinh Khang
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  3. Nguyen Minh Huy
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Corresponding authors

Correspondence to Vo Duc Quang or Tran Dinh Khang .

Editor information

Editors and Affiliations

  1. HCMC University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Koganei-shi, Tokyo, Japan

    Makoto Takizawa

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Quang, V.D., Khang, T.D., Huy, N.M. (2021). Improving ADABoost Algorithm with Weighted SVM for Imbalanced Data Classification. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-91387-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91386-1

  • Online ISBN: 978-3-030-91387-8

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