Abstract
Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority Oversampling Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each training sample into a low-dimensional space, and makes clustering of their low-dimensional representations. It then assigns weight to each minority sample as the product of two quantities: local minority density and local majority count, indicating its importance of sampling. The synthetic minority class samples are generated inside some minority cluster. MOT2LD has been evaluated on 15 real-world data sets. The experimental results have shown that our method outperforms some other existing methods including SMOTE, Borderline-SMOTE, ADASYN, and MWMOTE, in terms of G-mean and F-measure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fawcett, T.E., Provost, F.: Adaptive Fraud Detection. Data Min. Knowl. Disc. 3(1), 291–316 (1997)
Mladenić, D., Grobelnik, M.: Feature selection for unbalanced class distribution and naive bayes. In: Proceedings of the 16th International Conference on Machine Learning, pp. 258−267 (1999)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Han, H., Wang, W.Y., Mao, B.H.: Borderline-SMOTE: a new oversampling method in imbalanced data sets learning. In: Proceedings of International Conference on Intelligent Computing, pp. 878−887 (2005)
He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of IEEE International Joint Conference on Neural Networks, pp. 1322−1328 (2008)
Barua, S., Islam, M.M., Yao, X., Murase, K.: MWMOTE - majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (2014)
van der Maaten, L.J.P., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review. Tilburg University Techical Report, TiCC-TR 2009–005 (2009)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933)
Torgerson, W.S.: Multidimensional scaling I: theory and method. Psychometrika 17, 401–419 (1952)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, vol. 15, pp. 833−840 (2002)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2579–2605 (2008)
Liu, Y.: Distance metric learning: a comprehensive survey. Research Report, Michigan State University (2006)
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Inf. Process. Manage. 22(6), 465–476 (1986)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)
MacQueen, J.: Some methods for classifications and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematics, Statistics and Probability, University of California Press, pp. 281−297 (1967)
Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, 2013 [http://archive.ics.uci.edu/ml]
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC press (1984)
Cao, H., Li, X.L., Woon, Y.-K., Ng, S.K.: SPO: structure preserving oversampling for imbalanced time series classification. In: Proceedings of IEEE International Conference on Data Mining (2011)
Cao, H., Li, X.L., Woon, Y.K., Ng, S.K.: Integrated oversampling for imbalanced time series classification. IEEE Trans. Knowl. Data Eng. 25(12), 2809–2822 (2013)
Pang, Z.F., Cao, H., Tan, Y.F.: MOGT: oversampling with a parsimonious mixture of Gaussian trees model for imbalanced time-series classification. In: Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1−6 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xie, Z., Jiang, L., Ye, T., Li, X. (2015). A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9050. Springer, Cham. https://doi.org/10.1007/978-3-319-18123-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-18123-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18122-6
Online ISBN: 978-3-319-18123-3
eBook Packages: Computer ScienceComputer Science (R0)