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2018 | OriginalPaper | Chapter

A SMOTE Extension for Balancing Multivariate Epilepsy-Related Time Series Datasets

Authors : Enrique de la Cal, José R. Villar, Paula Vergara, Javier Sedano, Álvaro Herrero

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

In some cases, big data bunches are in the form of Time Series (TS), where the occurrence of complex TS events are rarely presented. In this scenario, learning algorithms need to cope with the TS data balancing problem, which has been barely studied for TS datasets. This research addresses this issue, describing a very simple TS extension of the well-known SMOTE algorithm for balancing datasets. To validate the proposal, it is applied to a realistic dataset publicly available containing epilepsy-related TS. A study on the characteristics of the dataset before and after the performance of this TS balancing algorithm is performed, showing evidence on the requirements for the research on this topic, the energy efficiency of the algorithm and the TS generation process among them.

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previous chapter Sparse Representation Based Anomalies Detection in Electrocardiography Signals
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Literature
1.
Beniczky, S., Polster, T., Kjaer, T., Hjalgrim, H.: Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia 4(54), e58–61 (2013)CrossRef
2.
Villar, J.R., González, S., Sedano, J., Chira, C., Trejo-Gabriel-Galán, J.M.: Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst. 25(4), 1450036–1450055 (2015)CrossRef
3.
Villar, J.R., Vergara, P., Menéndez, M., de la Cal, E., González, V.M., Sedano, J.: Generalized models for the classification of abnormal movements in daily life and its applicability to epilepsy convulsion recognition. Int. J. Neural Syst. 26(6) (2016). https://​doi.​org/​10.​1142/​S012906571650037​4
4.
Villar, J.R., Menéndez, M., de la Cal, E., González, V.M., Sedano, J.: Identification of abnormal movements with 3D accelerometer sensors for its application to seizure recognition. Int. J. Appl. Logic (2016). Accepted for publication
5.
López, V., Fernández, A., del Jesus, M., Herrera, F.: A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets. Knowl.-Based Syst. 38, 85–104 (2013)CrossRef
6.
Galar, M., Fernández, A., Barrenechea, E., Herrera, F.: Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn. 46(12), 3460–3471 (2013)CrossRef
7.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res., 321–357 (2002)
8.
Batista, G., Prati, R., Monard, M.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. 6, 20–29 (2004)CrossRef
9.
He, H., Bai, Y., Garcia, E., Li, S., et al.: 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)
10.
Tang, S., Chen, S.: The generation mechanism of synthetic minority class examples. In: Proceedings of 5th International Conference on Information Technology and Applications in Biomedicine (ITAB 2008), pp. 444–447 (2008)
11.
Stefanowski, J., Wilk, S.: Selective pre-processing of imbalanced data for improving classification performance. In: Proceedings of the 10th International Conference in Data Warehousing and Knowledge Discovery (DaWaK2008), vol. LNCS 5182, pp. 283–292. Springer (2008)
12.
Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)CrossRef
13.
Mishra, S., Saravanan, C., Dwivedi, V., Pathak, K.: Discovering flood rising pattern in hydrological time series data mining during the pre monsoon period. Indian J. Mar. Sci. 44(3), 3 (2015)
14.
Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2015)MATH
15.
Moses, D., et al.: A survey of data mining algorithms used in cardiovascular disease diagnosis from multi-lead ecg data. Kuwait J. Sci. 42(2) (2015)
16.
Köknar-Tezel, S., Latecki, L.J.: Improving SVM classification on imbalanced time series data sets with ghost points. Knowl. Inf. Syst. 28(1), 1–23 (2011)CrossRef
17.
Agrawal, A., Viktor, H.L., Paquet, E.: SCUT: multi-class imbalanced data classification using smote and cluster-based undersampling. In: Proceedings of 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) (2015)
18.
Phan, S., Famili, F., Tang, Z., Pan, Y., Liu, Z., Ouyang, J., Lenferink, A., Oconnor, M.M.C.: A novel pattern based clustering methodology for time-series microarray data. Int. J. Comput. Mathe. 84(5), 585–597 (2007)MathSciNetCrossRefMATH
19.
Metadata
Title
A SMOTE Extension for Balancing Multivariate Epilepsy-Related Time Series Datasets
Authors
Enrique de la Cal
José R. Villar
Paula Vergara
Javier Sedano
Álvaro Herrero
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_43

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