CCR: A combined cleaning and resampling algorithm for imbalanced data classification

Open access


Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.

Aggarwal, C.C., Hinneburg, A. and Keim, D.A. (2001). On the surprising behavior of distance metrics in high dimensional space, International Conference on Database Theory, London, UK, pp. 420-434.

Alcalá, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L. and Herrera, F. (2010). KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, Journal of Multiple- Valued Logic and Soft Computing 17(2-3): 255-287.

Barua, S., Islam, M.M., Yao, X. and Murase, K. (2014). MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning, IEEE Transactions on Knowledge and Data Engineering 26(2): 405-425.

Batista, G.E., Prati, R.C. and Monard, M.C. (2004). A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explorations Newsletter 6(1): 20-29.

Bunkhumpornpat, C. and Sinapiromsaran, K. (2015). CORE: Core-based synthetic minority over-sampling and borderline majority under-sampling technique, Inter national Journal of Data Mining and Bioinformatics 12(1): 44-58.

Bunkhumpornpat, C., Sinapiromsaran, K. and Lursinsap, C. (2009). Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand, pp. 475-482.

Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P. (2002). SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research 16: 321-357.

Chawla, N.V., Lazarevic, A., Hall, L.O. and Bowyer, K.W. (2003). SMOTEBoost: Improving prediction of the minority class in boosting, European Conference on Principles of Data Mining and Knowledge Discovery, Cavtat/ Dubrovnik, Croatia, pp. 107-119.

Dubey, R., Zhou, J., Wang, Y., Thompson, P.M. and Ye, J. (2014). Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study, NeuroImage 87: 220-241.

Estabrooks, A., Jo, T. and Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets, Computational Intelligence 20(1): 18-36.

Fernández, A., López, V., Galar, M., Del Jesus, M.J. and Herrera, F. (2013). Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches, Knowledge-Based Systems 42: 97-110.

Fernández-Navarro, F., Hervás-Martínez, C. and Gutiérrez, P.A. (2011). A dynamic over-sampling procedure based on sensitivity for multi-class problems, Pattern Recognition 44(8): 1821-1833.

Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F. (2012). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics C: Applications and Reviews 42(4): 463-484.

Galar, M., Fernández, A., Barrenechea, E. and Herrera, F. (2013). EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling, Pattern Recognition 46(12): 3460-3471.

García, S. and Herrera, F. (2009). Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy, Evolutionary Computation 17(3): 275-306.

García, V., Sánchez, J. and Mollineda, R. (2007). An empirical study of the behavior of classifiers on imbalanced and overlapped data sets, Iberoamerican Congress on Pattern Recognition, Valparaiso, Chile, pp. 397-406.

Han, H., Wang, W.-Y. and Mao, B.-H. (2005). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning, International Conference on Intelligent Computing, Hefei, China, pp. 878-887.

Hao, M., Wang, Y. and Bryant, S.H. (2014). An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data, Analytica Chimica Acta 806: 117-127.

He, H., Bai, Y., Garcia, E.A. and Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning, 2008 IEEE International Joint Conference on Neural Networks (IEEEWorld Congress on Computational Intelligence), Hong Kong, China, pp. 1322-1328.

He, H. and Garcia, E.A. (2009). Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering 21(9): 1263-1284.

Hoens, T.R., Polikar, R. and Chawla, N.V. (2012). Learning from streaming data with concept drift and imbalance: An overview, Progress in Artificial Intelligence 1(1): 89-101.

Jo, T. and Japkowicz, N. (2004). Class imbalances versus small disjuncts, ACM SIGKDD Explorations Newsletter 6(1): 40-49.

Khreich, W., Granger, E., Miri, A. and Sabourin, R. (2010). Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs, Pattern Recognition 43(8): 2732-2752.

Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions, Progress in Artificial Intelligence 5(4): 221-232.

Laurikkala, J. (2001). Improving identification of difficult small classes by balancing class distribution, Conference on Artificial Intelligence in Medicine in Europe, Cascais, Portugal, pp. 63-66.

Lemaitre, G., Nogueira, F. and Aridas, C.K. (2017). Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning, Journal of Machine Learning Research 18(17): 1-5.

Liu, X.-Y., Wu, J. and Zhou, Z.-H. (2009). Exploratory undersampling for class-imbalance learning, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 39(2): 539-550.

Liu, Y.-H. and Chen, Y.-T. (2005). Total margin based adaptive fuzzy support vector machines for multiview face recognition, 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA, Vol. 2, pp. 1704-1711.

López, V., Fernández, A., García, S., Palade, V. and Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences 250: 113-141.

Maciejewski, T. and Stefanowski, J. (2011). Local neighbourhood extension of SMOTE for mining imbalanced data, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, France, pp. 104-111.

Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A. and Tourassi, G.D. (2008). Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance, Neural Networks 21(2): 427-436.

Napierała, K. and Stefanowski, J. (2012). Identification of different types of minority class examples in imbalanced data, International Conference on Hybrid Artificial Intelligence Systems, Salamanca, Spain, pp. 139-150.

Napierała, K., Stefanowski, J. and Wilk, S. (2010). Learning from imbalanced data in presence of noisy and borderline examples, International Conference on Rough Sets and Current Trends in Computing, Warsaw, Poland, pp. 158-167.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R. and Dubourg, V. (2011). Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12(Oct): 2825-2830.

Prati, R.C., Batista, G. and Monard, M.C. (2004). Class imbalances versus class overlapping: An analysis of a learning system behavior, Mexican International Conference on Artificial Intelligence, Mexico City, Mexico, pp. 312-321.

Ramentol, E., Verbiest, N., Bello, R., Caballero, Y., Cornelis, C. and Herrera, F. (2012). SMOTE-FRST: A new resampling method using fuzzy rough set theory, 10th International FLINS Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making, Istanbul, Turkey.

Sáez, J. A., Galar, M., Luengo, J. and Herrera, F. (2013). Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness, Information Sciences 247: 1-20.

Sanz, J.A., Bernardo, D., Herrera, F., Bustince, H. and Hagras, H. (2015). A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data, IEEE Transactions on Fuzzy Systems 23(4): 973-990.

Stefanowski, J. (2016). Dealing with data difficulty factors while learning from imbalanced data, in S. Matwin and J. Mielniczuk (Eds.), Challenges in Computational Statistics and Data Mining, Springer, Heilderberg, pp. 333-363.

Stefanowski, J. and Wilk, S. (2008). Selective pre-processing of imbalanced data for improving classification performance, International Conference on Data Warehousing and Knowledge Discovery, Turin, Italy, pp. 283-292.

Sun, Y., Wong, A.K. and Kamel, M.S. (2009). Classification of imbalanced data: A review, International Journal of Pattern Recognition and Artificial Intelligence 23(04): 687-719.

Tomek, I. (1976). Two modifications of CNN, IEEE Transactions on Systems, Man, and Cybernetics 6(11): 769-772.

Triguero, I., del Río, S., López, V., Bacardit, J., Benítez, J.M. and Herrera, F. (2015). ROSEFW-RF: The winner algorithm for the ECBDL14 big data competition. An extremely imbalanced big data bioinformatics problem, Knowledge-Based Systems 87: 69-79.

Van Hulse, J., Khoshgoftaar, T.M. and Napolitano, A. (2007). Skewed class distributions and mislabeled examples, 7th IEEE International Conference on Data Mining Workshops (ICDMW 2007), Omaha, NE, USA, pp. 477-482.

Verbiest, N., Ramentol, E., Cornelis, C. and Herrera, F. (2014). Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection, Applied Soft Computing 22: 511-517.

Wang, S. and Yao, X. (2012). Multiclass imbalance problems: Analysis and potential solutions, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 42(4): 1119-1130.

Wei, W., Li, J., Cao, L., Ou, Y. and Chen, J. (2013). Effective detection of sophisticated online banking fraud on extremely imbalanced data, World Wide Web 16(4): 449-475.

Wilson, D.L. (1972). Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions on Systems, Man, and Cybernetics 2(3): 408-421.

Yu, H., Ni, J. and Zhao, J. (2013). ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data, Neurocomputing 101: 309-318.

Zhang, H. and Li, M. (2014). RWO-sampling: A random walk over-sampling approach to imbalanced data classification, Information Fusion 20: 99-116.

Zhang, Z., Krawczyk, B., García, S., Rosales-Pérez, A. and Herrera, F. (2016). Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data, Knowledge-Based Systems 106: 251-263.

International Journal of Applied Mathematics and Computer Science

Journal of the University of Zielona Góra

Journal Information

IMPACT FACTOR 2017: 1.694
5-year IMPACT FACTOR: 1.712

CiteScore 2017: 2.20

SCImago Journal Rank (SJR) 2017: 0.729
Source Normalized Impact per Paper (SNIP) 2017: 1.604

Mathematical Citation Quotient (MCQ) 2017: 0.13

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 315 264 22
PDF Downloads 157 149 18