Abstract
Imbalanced data classification is an important issue in machine learning. To solve the data imbalance problem, rebalance algorithms are utilized. However, the rebalance algorithm has a lot of problems. Hence, the specific classification algorithm without a rebalancing algorithm is required. In this paper, we apply the zero-shot classifier to imbalance problem. The zero-shot classifier works despite the number of minority class is zero. Hence, the zero-shot classifier should also work in weaker data imbalance problems. We utilize the cluster-based zero-shot learning algorithm to do imbalanced data classification. The proposed method is evaluated using an imbalanced-learn dataset and compared with Decision Tree, K-Nearest Neighbor, and Over-sampling methods.
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References
Sun, J., Lang, J., Fujita, H., Li, H.: Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Inf. Sci. 425, 76–91 (2018)
Sanz, E.P., Gómez Hidalgo, J.M., Cortizo Pérez, J.C.: Chapter 3 Email spam filtering. In: Advances in Computers, vol. 74, pp. 45–114 (2008)
Li, D.-C., Liu, C.-W., Hu, S.C.: A learning method for the class imbalance problem with medical data sets. Comput. Biol. Med. 40, 509–518 (2010)
Ambai, K., Fujita, H.: MNDO: Multivariate Normal Distribution Based Over-Sampling for Binary Classification. Frontiers in Artificial Intelligence and Applications, Volume 303: New Trends in Intelligent Software Methodologies, Tools and Techniques, pp. 425–438 (2018)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
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 (IJCNN 2008), pp. 1322–1328 (2008)
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
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Commun. 2(3), 408–421 (1972)
Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6, 769–772 (2010)
Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225–256 (2013). https://doi.org/10.1007/s10994-013-5422-z
Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol. 97, pp. 179–186 (1997)
Hayashi, T., Fujita, H.: Cluster-based zero-shot learning for multivariate data. J. Ambient Intell. Humanized Comput. (2020). https://doi.org/10.1007/s12652-020-02268-5
Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18, 1–5 (2017)
Zhang, C., et al.: Multi-imbalance: an open-source software for multi-class imbalance learning. Knowl.-Based Syst. 174, 137–143 (2019)
Bia, J., Zhang, C.: An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowl.-Based Syst. 158, 81–93 (2018)
Wang, W., Zheng, V.W., Yu, H., Miao, C.: A survey of zero-shot learning: settings, methods, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2) (2019). Article 13. https://doi.org/10.1145/3293318
Socher, R., Ganjoo, M., Manning, C.D., Ng, A.Y.: Zero-shot learning through cross-modal transfer. In: NIPS 2013 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 1, pp. 935–943 (2013)
Sun, J., Li, H., Fujita, H., Binbin, F., Ai, W.: Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Inform. Fusion 54, 128–144 (2020)
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Hayashi, T., Ambai, K., Fujita, H. (2020). Applying Cluster-Based Zero-Shot Classifier to Data Imbalance Problems. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_65
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DOI: https://doi.org/10.1007/978-3-030-55789-8_65
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