Abstract
The difficulty of the many practical decision problem lies in the nature of analyzed data. One of the most important real data characteristic is imbalance among examples from different classes. Despite more than two decades of research, imbalanced data classification is still one of the vital challenges to be addressed. The traditional classification algorithms display strongly biased performance on imbalanced datasets. One of the most popular way to deal with such a problem is to modify the learning set to decrease disproportion between objects from different classes using over- or undersampling approaches. In this work a novel preprocessing technique for imbalanced datasets is presented, which takes into consideration the mutual density class distribution. The proposed approach has been evaluated on the basis of the computer experiments carried out on the benchmark datasets. Their results seem to confirm the usefulness of the proposed concept in comparison to the state-of-art methods.
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
References
Ahmed, F., Samorani, M., Bellinger, C., Zaïane, O.R.: Advantage of integration in big data: Feature generation in multi-relational databases for imbalanced learning. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, 5–8 December 2016, pp. 532–539 (2016)
Alcalá, 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 (2010)
Bellinger, C., Sharma, S., Japkowicz, N.: One-class versus binary classification: Which and when? In: 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, 12–15 December 2012, vol. 2. pp. 102–106 (2012)
Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49(2), 31:1–31:50 (2016)
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). doi:10.1007/978-3-642-01307-2_43
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)
Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS, vol. 2838, pp. 107–119. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39804-2_12
Domingos, P.M.: Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 15–18 August 1999, pp. 155–164 (1999)
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). doi:10.1007/11538059_91
He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008. (IEEE World Congress on Computational Intelligence), pp. 1322–1328. IEEE (2008)
Porwik, P., Doroz, R., Orczyk, T.: Signatures verification based on PNN classifier optimised by PSO algorithm. Pattern Recogn. 60, 998–1014 (2016)
Ramentol, E., Caballero, Y., Bello, R., Herrera, F.: SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. Knowl. Inf. Syst. 33(2), 245–265 (2012)
Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6(11), 769–772 (1976)
Triguero, I., Galar, M., Merino, D., Maillo, J., Bustince, H., Herrera, F.: Evolutionary undersampling for extremely imbalanced big data classification under apache spark. In: IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 640–647 (2016)
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 2(3), 408–421 (1972)
Wozniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)
Acknowledgements
This work was supported by the Polish National Science Center under the grant no. UMO-2015/19/B/ST6/01597 as well as the PLGrid Infrastructure.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Koziarski, M., Krawczyk, B., Woźniak, M. (2017). Radial-Based Approach to Imbalanced Data Oversampling. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-59650-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59649-5
Online ISBN: 978-3-319-59650-1
eBook Packages: Computer ScienceComputer Science (R0)