Optimization of data resampling through GA for the classification of imbalanced datasets

(1) * Filippo Galli Mail (TeCIP Institute, Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(2) Marco Vannucci Mail (TeCIP Institute, Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(3) Valentina Colla Mail (TeCIP Institute, Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
*corresponding author

Abstract


Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This paper overview a novel family of methods for the resampling of an imbalanced dataset in order to maximize the performance of arbitrary data-driven classifiers. The presented approaches exploit genetic algorithms (GA) for the optimization of the data selection process according to a set of criteria that assess each candidate sample suitability. A comparison among the presented techniques on a set of industrial and literature datasets put into evidence the validity of this family of approaches, which is able not only to improve the performance of a standard classifier but also to determine the optimal resampling rate automatically. Future activities for the improvement of the proposed approach will include the development of new criteria for the assessment of sample suitability.

Keywords


Imbalanced datasets; Classification; Data resampling; Genetic algorithm

   

DOI

https://doi.org/10.26555/ijain.v5i3.409
      

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[1] A. Borselli, V. Colla, M. Vannucci, and M. Veroli, “A fuzzy inference system applied to defect detection in flat steel production,” in International Conference on Fuzzy Systems, 2010, pp. 1–6, doi: 10.1109/FUZZY.2010.5584036.

[2] M. Vannucci and V. Colla, “Classification of Unbalanced Datasets and Detection of Rare Events in Industry: Issues and Solutions,” 2016, pp. 337–351, doi: 10.1007/978-3-319-44188-7_26.

[3] M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, and G. D. Tourassi, “Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance,” Neural Networks, vol. 21, no. 2–3, pp. 427–436, Mar. 2008, doi: 10.1016/j.neunet.2007.12.031.

[4] J.-J. Liao, C.-H. Shih, T.-F. Chen, and M.-F. Hsu, “An ensemble-based model for two-class imbalanced financial problem,” Econ. Model., vol. 37, pp. 175–183, Feb. 2014, doi: 10.1016/j.econmod.2013.11.013.

[5] N. S. Sani, M. Abdul Rahman, A. Abu Bakar, S. Sahran, and H. Mohd Sarim, “Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 4–2, p. 1698, Sep. 2018, doi: 10.18517/ijaseit.8.4-2.6829.

[6] Haibo He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, Sep. 2009, doi: 10.1109/TKDE.2008.239.

[7] A. Estabrooks, T. Jo, and N. Japkowicz, “A Multiple Resampling Method for Learning from Imbalanced Data Sets,” Comput. Intell., vol. 20, no. 1, pp. 18–36, Feb. 2004, doi: 10.1111/j.0824-7935.2004.t01-1-00228.x.

[8] N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study1,” Intell. Data Anal., vol. 6, no. 5, pp. 429–449, Nov. 2002, doi: 10.3233/IDA-2002-6504.

[9] Y. Sun, A. K. C. Wong, and M. S. Kamel, “Classification of Imbalanced Data: a Review,” Int. J. Pattern Recognit. Artif. Intell., vol. 23, no. 04, pp. 687–719, Jun. 2009, doi: 10.1142/S0218001409007326.

[10] M. Vannucci, V. Colla, M. Sgarbi, and O. Toscanelli, “Thresholded Neural Networks for Sensitive Industrial Classification Tasks,” 2009, pp. 1320–1327, doi: 10.1007/978-3-642-02478-8_165.

[11] M. Vannucci and V. Colla, “Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic,” Appl. Soft Comput., vol. 11, no. 2, pp. 2383–2390, Mar. 2011, doi: 10.1016/j.asoc.2010.09.001.

[12] V. Soler and M. Prim, “Rectangular Basis Functions Applied to Imbalanced Datasets,” 2007, pp. 511–519, doi: 10.1007/978-3-540-74690-4_52.

[13] Yuchun Tang, Yan-Qing Zhang, N. V. Chawla, and S. Krasser, “SVMs Modeling for Highly Imbalanced Classification,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 39, no. 1, pp. 281–288, Feb. 2009, doi: 10.1109/TSMCB.2008.2002909.

[14] R. Batuwita and V. Palade, “FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning,” IEEE Trans. Fuzzy Syst., vol. 18, no. 3, pp. 558–571, Jun. 2010, doi: 10.1109/TFUZZ.2010.2042721.

[15] Y. Sun, M. S. Kamel, A. K. C. Wong, and Y. Wang, “Cost-sensitive boosting for classification of imbalanced data,” Pattern Recognit., vol. 40, no. 12, pp. 3358–3378, Dec. 2007, doi: 10.1016/j.patcog.2007.04.009.

[16] Z. Yuan, D. Bao, Z. Chen, and M. Liu, “Integrated Transfer Learning Algorithm Using Multi-source TrAdaBoost for Unbalanced Samples Classification,” in 2017 International Conference on Computing Intelligence and Information System (CIIS), 2017, pp. 188–195, doi: 10.1109/CIIS.2017.37.

[17] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, Jun. 2002, doi: 10.1613/jair.953.

[18] V. García, J. S. Sánchez, and R. A. Mollineda, “On the effectiveness of preprocessing methods when dealing with different levels of class imbalance,” Knowledge-Based Syst., vol. 25, no. 1, pp. 13–21, Feb. 2012, doi: 10.1016/j.knosys.2011.06.013.

[19] F. Charte, A. J. Rivera, M. J. del Jesus, and F. Herrera, “Addressing imbalance in multilabel classification: Measures and random resampling algorithms,” Neurocomputing, vol. 163, pp. 3–16, Sep. 2015, doi: 10.1016/j.neucom.2014.08.091.

[20] G. E. A. P. A. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD Explor. Newsl., vol. 6, no. 1, p. 20, Jun. 2004, doi: 10.1145/1007730.1007735.

[21] J. Laurikkala, “Improving Identification of Difficult Small Classes by Balancing Class Distribution,” 2001, pp. 63–66, doi: 10.1007/3-540-48229-6_9.

[22] N. Japkowicz, “The Class Imbalance Problem: Significance and Strategies,” Proc. 2000 Int. Conf. Artif. Intell., 2000, doi: 10.1.1.35.1693.

[23] S.-J. Yen and Y.-S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,” Expert Syst. Appl., vol. 36, no. 3, pp. 5718–5727, Apr. 2009, doi: 10.1016/j.eswa.2008.06.108.

[24] E. Ramentol, Y. Caballero, R. Bello, and F. Herrera, “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., vol. 33, no. 2, pp. 245–265, Nov. 2012, doi: 10.1007/s10115-011-0465-6.

[25] S. Cateni, V. Colla, and M. Vannucci, “A method for resampling imbalanced datasets in binary classification tasks for real-world problems,” Neurocomputing, vol. 135, pp. 32–41, Jul. 2014, doi: 10.1016/j.neucom.2013.05.059.

[26] H. Hartono, O. S. Sitompul, T. Tulus, and E. B. Nababan, “Biased support vector machine and weighted-smote in handling class imbalance problem,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, p. 21, Mar. 2018, doi: 10.26555/ijain.v4i1.146.

[27] M. Vannucci and V. Colla, “Smart Under-Sampling for the Detection of Rare Patterns in Unbalanced Datasets,” 2016, pp. 395–404, doi: 10.1007/978-3-319-39630-9_33.

[28] M. Vannucci and V. Colla, “Genetic Algorithms Based Resampling for the Classification of Unbalanced Datasets,” 2018, pp. 23–32, doi: 10.1007/978-3-319-59424-8_3.

[29] M. Vannucci and V. Colla, “Imbalanced Datasets Resampling Through Self Organizing Maps and Genetic Algorithms,” 2019, pp. 399–411, doi: 10.1007/978-3-030-20257-6_34.

[30] K. Bache and M. Lichman, “UCI Machine Learning Repository, University of California, School of Information and Computer Science,” Irvine, CA, 2013, available at : http://archive.ics.uci.edu/ml.

[31] S. Cateni, V. Colla, and M. Vannucci, “A Hybrid Feature Selection Method for Classification Purposes,” in 2014 European Modelling Symposium, 2014, pp. 39–44, doi: 10.1109/EMS.2014.44.

[32] S. Cateni, V. Colla, and M. Vannucci, “A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a SOM-based classifier,” Int. J. Simul. Syst. Sci. Technol., 2011, available at: Google Scholar .

[33] S. Cateni, V. Colla, and M. Vannucci, “General Purpose Input Variables Extraction: A Genetic Algorithm Based Procedure GIVE A GAP,” in 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009, pp. 1278–1283, doi: 10.1109/ISDA.2009.190.

[34] M. Sgarbi, V. Colla, S. Cateni, and S. Higson, “Pre-processing of data coming from a laser-EMAT system for non-destructive testing of steel slabs,” ISA Trans., vol. 51, no. 1, pp. 181–188, Jan. 2012, doi: 10.1016/j.isatra.2011.07.004.




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