Abstract
Data level technique is proved to be effective in imbalance learning. The SMOTE is a famous oversampling technique generating synthetic minority samples by linear interpolation between adjacent minorities. However, it becomes inefficiency for datasets with sparse distributions. In this paper, we propose the Stochastic Sensitivity Oversampling (SSO) which generates synthetic samples following Gaussian distributions in the Q-union of minority samples. The Q-union is the union of Q-neighborhoods (hypercubes centered at minority samples) and such that new samples are synthesized around minority samples. Experimental results show that the proposed algorithm performs well on most of datasets, especially those with a sparse distribution.
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References
He, H., Garcia, E.: Learning from imbalanced data. IEEE Transactions on Data and Knowledge Engineering 21(9), 1263–1284 (2009)
Ng, W.W.Y., He, Z.-M., Yeung, D.S., Chan, P.P.K.: Steganalysis Classifier Training via Minimizing Sensitivity for Different Imaging Source. Information Science, 211–224 (2014)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic Minority Over-sampling Technique. J. of Artifical Intelligence Research 16, 341–378 (2002)
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)
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, vol. 5476, pp. 475–482. Springer, Heidelberg (2009)
Maciejewski, T., Stefanowski, J.: Local neighbourhood extension of smote for mining imbalanced data. In: Proceedings of 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 104 –111 (2011)
Yeung, D.S., Ng, W.W.Y., Wang, D., Tsang, E.C.C., Wang, X.-Z.: Localized Generalization Error and Its Application to Architecture Selection for Radial Basis Function Neural Network. IEEE Trans. on Neural Networks 18, 1294–1305 (2007)
Tejchman, J., Kozicki, J.: General. In: Tejchman, J., Kozicki, J. (eds.) Experimental and Theoretical Investigations of Steel-Fibrous Concrete. SSGG, vol. 3, pp. 3–26. Springer, Heidelberg (2010)
Chan, P.P.K., Ng, W.W.Y., Yeung, D.S.: Active Learning using Localized Generalization Error of Candidate Sample as Criterion. In: IEEE Proceedings of International Conference on Systems, Man and Cybernetics, pp. 3604–3609 (2005)
Yeung, D.S., Zhang, Y., Ng, W.W.Y., Chen, Q.-C.: Active Learning using Localized Generalization Error for Text Categorization. In: Proceedings of International Conference on Machine Learning and Cybernetics, pp. 2686–2691 (2006)
Ng, W.W.Y., Yeung, D.S., Cloete, I.: Input Sample Selection for RBF Neural Network Classification Problems using Sensitivity Measure In: IEEE Proceedings of International Conference on Systems, Man and Cybernetics, pp. 2593—2598 (2003)
Wilson D.R., Martinez, TR.: Improved heterogeneous distance functions. arXiv preprint cs/9701101 (1997)
Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge University (2003)
Sun, B., Ng, W.W.Y., Yeung, D.S., Wang, J.: Localized Generalization Error based Active Learning for Image Annotation. In: IEEE Proceedings of International Conference on Systems, Man and Cybernetics, pp. 60–65 (2008)
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Data Mining Researchers Technical Report HPL-2003-4, HP Labs (2003)
Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., et al.: RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(1), 185–197 (2010)
Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 539–550 (2009)
Mani, I, Zhang, I.: kNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of Workshop on Learning from Imbalanced Datasets (2003)
Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One-sided Selection//ICML. 97, 179–186 (1997)
Van Hulse, J., Khoshgoftaar, T.: Knowledge discovery from imbalanced and noisy data. Data & Knowledge Engineering 68(12), 1513–1542 (2009)
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Rong, T., Gong, H., Ng, W.W.Y. (2014). Stochastic Sensitivity Oversampling Technique for Imbalanced Data. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_18
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DOI: https://doi.org/10.1007/978-3-662-45652-1_18
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