Abstract
Addressing the huge amount of data continuously generated is an important challenge in the Machine Learning field. The need to adapt the traditional techniques or create new ones is evident. To do so, distributed technologies have to be used to deal with the significant scalability constraints due to the Big Data context.
In many Big Data applications for classification, there are some classes that are highly underrepresented, leading to what is known as the imbalanced classification problem. In this scenario, learning algorithms are often biased towards the majority classes, treating minority ones as outliers or noise.
Consequently, preprocessing techniques to balance the class distribution were developed. This can be achieved by suppressing majority instances (undersampling) or by creating minority examples (oversampling). Regarding the oversampling methods, one of the most widespread is the SMOTE algorithm, which creates artificial examples according to the neighborhood of each minority class instance.
In this work, our objective is to analyze the SMOTE behavior in Big Data as a function of some key aspects such as the oversampling degree, the neighborhood value and, specially, the type of distributed design (local vs. global).
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
Notes
- 1.
The SMOTE variants are abbreviated as “SMT-BD” or “SMT-MR” in all tables.
References
Chen, C.L.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)
Prati, R.C., Batista, G.E.A.P.A., Silva, D.F.: Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowl. Inf. Syst. 45(1), 247–270 (2015)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design and Implementation, OSDI 2004, vol. 6, p. 10. USENIX Association, Berkeley (2004)
Ramírez-Gallego, S., Fernández, A., García, S., Chen, M., Herrera, F.: Big data: tutorial and guidelines on information and process fusion for analytics algorithms with mapreduce. Inf. Fusion 42, 51–61 (2018)
García-Gil, D., Luengo, J., García, S., Herrera, F.: Enabling smart data: noise filtering in big data classification. Inf. Sci. 479, 135–152 (2019)
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)
Fernandez, A., Garcia, S., Herrera, F., Chawla, N.V.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018)
White, T.: Hadoop: The Definitive Guide, 4th edn. O’Reilly Media, Sebastopol (2015)
Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2012), pp. 15–28. USENIX, San Jose (2012)
Karau, H., Konwinski, A., Wendell, P., Zaharia, M.: Learning Spark: Lightning-Fast Big Data Analytics, 1st edn. O’Reilly Media, Sebastopol (2015)
Meng, X., et al.: MLlib: machine learning in apache spark. J. Mach. Learn. Res. 17(34), 1–7 (2016)
Zaharia, M., et al.: Discretized streams: fault-tolerant streaming computation at scale. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP 2013, pp. 423–438. ACM, New York (2013)
Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 6(1), 20–29 (2004)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250(20), 113–141 (2013)
Fernandez, A., del Rio, S., Chawla, N.V., Herrera, F.: An insight into imbalanced big data classification: Outcomes and challenges. Complex Intell. Syst. 3(2), 105–120 (2017)
Basgall, M.J., Hasperué, W., Naiouf, M., Fernández, A., Herrera, F.: SMOTE-BD: an exact and scalable oversampling method for imbalanced classification in big data. J. Comput. Sci. Technol. 18(03), e23 (2018)
SMOTE-BD Spark Package (2018). https://spark-packages.org/package/majobasgall/smote-bd
Maillo, J., Ramírez-Gallego, S., Triguero, I., Herrera, F.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl.-Based Syst. 117, 3–15 (2017)
SMOTE-MR source code (2018). https://github.com/majobasgall/smote-mr
Fernandez, A., Herrera, F., Cordon, O., Jose del Jesus, M., Marcelloni, F.: Evolutionary fuzzy systems for explainable artificial intelligence: why, when, what for, and where to? IEEE Comput. Intell. Mag. 14(1), 69–81 (2019)
Lichman, M.: UCI machine learning repository (2013)
Gutierrez, P.D., Lastra, M., Benitez, J.M., Herrera, F.: SMOTE-GPU: big data preprocessing on commodity hardware for imbalanced classification. Prog. Artif. Intell. 6(4), 347–354 (2017)
Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognit. 36(3), 849–851 (2003)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299–310 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Basgall, M.J., Hasperué, W., Naiouf, M., Fernández, A., Herrera, F. (2019). An Analysis of Local and Global Solutions to Address Big Data Imbalanced Classification: A Case Study with SMOTE Preprocessing. In: Naiouf, M., Chichizola, F., Rucci, E. (eds) Cloud Computing and Big Data. JCC&BD 2019. Communications in Computer and Information Science, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-030-27713-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-27713-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27712-3
Online ISBN: 978-3-030-27713-0
eBook Packages: Computer ScienceComputer Science (R0)