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An Approach to Imbalanced Data Classification Based on Instance Selection and Over-Sampling

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

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Abstract

The paper referees to a problem of learning from class-imbalanced data. The class imbalance problem arises when the number of instances from different classes differs substantially. Instance selection aims at deciding which instances from the training set should be retained and used during the learning process. Over-sampling is an approach dedicated to duplicate minority class instances. In the paper, a hybrid approach for the imbalanced data learning using the over-sampling and instance selection techniques is proposed. Instances are selected to reduce the number of instances belonging to the majority class, while the number of instances belonging to the minority class is expanded. The process of instance selection is based on clustering, where the authors’ approach to clustering and instance selection using an agent-based population learning algorithm is applied. As a result a more balanced distribution of instances belonging to different classes is obtained and a dataset size is reduced. The proposed approach is validated experimentally using several benchmark datasets.

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References

  1. Chawla, N.V., Japkowicz, N., Drive, P.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newsl. 6(1), 1–6 (2004)

    Article  Google Scholar 

  2. Sun, B., Chen, H., Wang, J., Xie, H.: Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Front. Comput. Sci. 12(2), 331–350 (2018)

    Article  Google Scholar 

  3. Fernandez, A., del Jesus, M.J., Herrera, F.: Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets. Int. J. Approximate Reasoning 50, 561–577 (2009). https://doi.org/10.1016/j.ijar.2008.11.004

    Article  MATH  Google Scholar 

  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 Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463–484 (2012)

    Article  Google Scholar 

  5. Lin, W.-C., Chih-Fong, T., Hu, Y.-H., Jhang, J.-S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409 (2017). http://doi.org/10.1016/j.ins.2017.05.008

    Article  Google Scholar 

  6. Kim, S.-W., Oommen, B.J.: A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6, 232–244 (2003)

    Article  MathSciNet  Google Scholar 

  7. Bhanu, B., Peng, J.: Adaptive integration image segmentation and object recognition. IEEE Trans. Syst. Man Cybern. 30(4), 427–441 (2000)

    Article  Google Scholar 

  8. Czarnowski, I., Jędrzejowicz, P.: A new cluster-based instance selection algorithm. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, Robert J., Jain, Lakhmi C. (eds.) KES-AMSTA 2011. LNCS (LNAI), vol. 6682, pp. 436–445. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22000-5_45

    Chapter  MATH  Google Scholar 

  9. Tsai, C.-F., Lin, W.-C., Hu, Y.-H., Ya, G.-T.: Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Inf. Sci. 477, 47–54 (2019). https://doi.org/10.1016/j.ins.2018.10.029

    Article  Google Scholar 

  10. Last, F., Douzas, G., Bacao, F., Oversampling for Imbalanced Learning Based on K-means and SMOTE, p. 19. CoRR abs/1711.00837 (2017)

    Google Scholar 

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

    Article  Google Scholar 

  12. Czarnowski, I., Jędrzejowicz, P.: Cluster-based instance selection for the imbalanced data classification. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11056, pp. 191–200. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98446-9_18

    Chapter  Google Scholar 

  13. Czarnowski, I.: Cluster-based instance selection for machine classification. Knowl. Inf. Syst. 30(1), 113–133 (2012)

    Article  Google Scholar 

  14. Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-smote: a new over-sampling method in imbalanced data sets learning. Adv. Intell. Comput. 17(12), 878–887 (2005)

    Google Scholar 

  15. Ma, L., Fan, S.: Cure-smote algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests. BMC Bioinf. 18(1), 169 (2017)

    Article  Google Scholar 

  16. Cieslak, D.A., Chawla, N.V., Striegel, A.: Combating imbalance in network intrusion datasets. In: Proceedings of the 2006 IEEE International Conference on Granular Computing, 2006, pp. 732–737. IEEE (2006)

    Google Scholar 

  17. Skryjomski, P., Krawczyk, B.: Influence of minority class instance types on SMOTE imbalanced data oversampling. In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR, vol. 74, pp. 7–21 (2017)

    Google Scholar 

  18. Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-Level-SMOTE: safe-level-synthetic minority over-sampling TEchnique for handling the class imbalanced problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 475–482. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_43

    Chapter  Google Scholar 

  19. Nejatian, S., Parvin, H., Faraji, E.: Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification. Neurocomputing 276(7), 55–66 (2018)

    Article  Google Scholar 

  20. Sowah, R.A., Agebure, M.A., Mills, G.A., Koumadi, K.M., Fiawoo, S.Y.: New cluster undersampling technique for class imbalance learning. Int. J. Mach. Learn. Comput. 6(3), 205–214 (2016). https://doi.org/10.18178/ijmlc.2016.6.3.599

    Article  Google Scholar 

  21. Jędrzejowicz, P.: Social learning algorithm as a tool for solving some difficult scheduling problems. Found. Comput. Decis. Sci. 24, 51–66 (1999)

    MathSciNet  MATH  Google Scholar 

  22. Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous teams: co-operation schemes for autonomous, computer-based agents. Technical report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)

    Google Scholar 

  23. Czarnowski, I., Jędrzejowicz, P.: An approach to data reduction and integrated machine classification. New Gener. Comput. 28(1), 21–40 (2010)

    Article  Google Scholar 

  24. Czarnowski, I., Jędrzejowicz, P.: Cluster integration for the cluster-based instance selection. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS (LNAI), vol. 6421, pp. 353–362. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16693-8_37

    Chapter  MATH  Google Scholar 

  25. Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple Valued Logic Soft Comput. 17(2–3), 255–287 (2011). Last accessed to the repository 2018/04/10

    Google Scholar 

  26. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, SanMateo (1993)

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Ireneusz Czarnowski & Piotr Jędrzejowicz

Authors
  1. Ireneusz Czarnowski
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  2. Piotr Jędrzejowicz
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Corresponding author

Correspondence to Ireneusz Czarnowski .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Czarnowski, I., Jędrzejowicz, P. (2019). An Approach to Imbalanced Data Classification Based on Instance Selection and Over-Sampling. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_50

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  • DOI: https://doi.org/10.1007/978-3-030-28377-3_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

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