Skip to main content
SpringerLink
Log in

Learning from Imbalanced Data: A Comparative Study

  • Conference paper
  • First Online:
Security and Privacy in Social Networks and Big Data (SocialSec 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1095))

Abstract

Learning from imbalanced data is a great challenge when we use machine learning techniques to solve real-world problems. Imbalanced data can result in a classifier’s sub-optimal performance. Moreover, the distribution of the testing data may differ from that of the training data, thus the true mis-classification costs is hard to predict at the time of learning. In this paper, we present a comparative study on various sampling techniques in terms of their effectiveness in improving machine learning performance against class imbalanced data sets. In particular, we evaluate ten sampling techniques such as random sampling, cluster-based sampling, and SMOTE. Two widely used machine learning algorithms are applied to train the base classifiers. For the purpose of evaluation, a number of data sets from different domains are used and the results are analysed based on different metrics.

Supported by Guangdong Power Grid Research Project 030000QQ00180019.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Barandela, R., Valdovinos, R.M., Sánchez, J.S., Ferri, F.J.: The imbalanced training sample problem: under or over sampling? In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR /SPR 2004. LNCS, vol. 3138, pp. 806–814. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27868-9_88

    Chapter  Google Scholar 

  2. Bekkar, M., Djemaa, H.K., Alitouche, T.A.: Evaluation measures for models assessment over imbalanced data sets. J. Inf. Eng. Appl. 3(10) (2013)

    Google Scholar 

  3. Bhowan, U., Johnston, M., Zhang, M.: Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans. Syst. Man Cybern. Part B 42(2), 406–421 (2012). https://doi.org/10.1109/TSMCB.2011.2167144

    Article  Google Scholar 

  4. Cano, A., Zafra, A., Ventura, S.: Weighted data gravitation classification for standard and imbalanced data. IEEE Trans. Cybern. 43(6), 1672–1687 (2013). https://doi.org/10.1109/TSMCB.2012.2227470

    Article  Google Scholar 

  5. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

  6. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  7. Domingos, P.: Metacost: a general method for making classifiers cost-sensitive. KDD 99, 155–164 (1999)

    Article  Google Scholar 

  8. Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005). https://doi.org/10.1007/11538059_91

    Chapter  Google Scholar 

  9. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009). https://doi.org/10.1109/TKDE.2008.239

    Article  Google Scholar 

  10. He, H., Shen, X.: A ranked subspace learning method for gene expression data classification. In: Arabnia, H.R., Yang, M.Q., Yang, J.Y. (eds.) Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007, Las Vegas, Nevada, USA, 25–28 June 2007, vol. I, pp. 358–364. CSREA Press (2007)

    Google Scholar 

  11. Hulse, J.V., Khoshgoftaar, T.M., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: Ghahramani, Z. (ed.) Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, 20–24 June 2007, vol. 227, pp. 935–942. ACM International Conference Proceeding Series, ACM (2007). https://doi.org/10.1145/1273496.1273614

  12. Japkowicz, N.: Supervised versus unsupervised binary-learning by feedforward neural networks. Mach. Learn. 42(1/2), 97–122 (2001). https://doi.org/10.1023/A:1007660820062

    Article  MATH  Google Scholar 

  13. Japkowicz, N., Myers, C., Gluck, M.A.: A novelty detection approach to classification. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 1995, Montréal Québec, Canada, 20–25 August 1995, vol. 2, pp. 518–523. Morgan Kaufmann (1995). http://ijcai.org/Proceedings/95-1/Papers/068.pdf

  14. Jo, T., Japkowicz, N.: Class imbalances versus small disjuncts. SIGKDD Explorations 6(1), 40–49 (2004). https://doi.org/10.1145/1007730.1007737

    Article  Google Scholar 

  15. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Fisher, D.H. (ed.) Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, 8–12 July 1997, pp. 179–186. Morgan Kaufmann (1997)

    Google Scholar 

  16. Lin, M., Tang, K., Yao, X.: Dynamic sampling approach to training neural networks for multiclass imbalance classification. IEEE Trans. Neural Netw. Learn. Syst. 24(4), 647–660 (2013). https://doi.org/10.1109/TNNLS.2012.2228231

    Article  Google Scholar 

  17. Quinlan, J.R.: C4.5: programs for machine learning. Elsevier (2014)

    Google Scholar 

  18. Rahman, M.M., Davis, D.: Cluster based under-sampling for unbalanced cardiovascular data. Proc. World Congr. Eng. 3, 3–5 (2013)

    Google Scholar 

  19. Raskutti, B., Kowalczyk, A.: Extreme re-balancing for svms: a case study. SIGKDD Explorations 6(1), 60–69 (2004). https://doi.org/10.1145/1007730.1007739

    Article  Google Scholar 

  20. Shirabad, J.S., Menzies, T.J.: The promise repository of software engineering databases. School of Information Technology and Engineering, University of Ottawa, Canada 24 (2005)

    Google Scholar 

  21. Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007). https://doi.org/10.1016/j.patcog.2007.04.009

    Article  MATH  Google Scholar 

  22. Wang, C., Hu, L., Guo, M., Liu, X., Zou, Q.: imdc: an ensemble learning method for imbalanced classification with mirna data. Genet. Mol. Res. 14(1), 123–133 (2015)

    Article  Google Scholar 

  23. Yang, S., Khot, T., Kersting, K., Kunapuli, G., Hauser, K., Natarajan, S.: Learning from imbalanced data in relational domains: a soft margin approach. In: Kumar, R., Toivonen, H., Pei, J., Huang, J.Z., Wu, X. (eds.) 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, 14–17 December 2014, pp. 1085–1090. IEEE Computer Society (2014). DOI: https://doi.org/10.1109/ICDM.2014.152

  24. Zhuang, L., Dai, H.: Parameter optimization of kernel-based one-class classifier on imbalance learning. JCP 1(7), 32–40 (2006). https://doi.org/10.4304/jcp.1.7.32-40

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Program and Research Center, Guangdong Power Grid Corporation, Guangzhou, China

    Yu Sui, Mengze Yu, Haifeng Hong & Xianxian Pan

Authors
  1. Yu Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengze Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haifeng Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianxian Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Sui .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

  2. University of Plymouth, Devon, UK

    Steven Furnell

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sui, Y., Yu, M., Hong, H., Pan, X. (2019). Learning from Imbalanced Data: A Comparative Study. In: Meng, W., Furnell, S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2019. Communications in Computer and Information Science, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-15-0758-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0758-8_20

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0757-1

  • Online ISBN: 978-981-15-0758-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation