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

  • Enrique de la Cal
  • José R. Villar
  • Paula Vergara
  • Javier Sedano
  • Álvaro Herrero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 649)

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.

Keywords

Dataset balancing algorithms SMOTE Time series 

Notes

Acknowledgment

This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2014-56967-R.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Enrique de la Cal
    • 1
  • José R. Villar
    • 1
  • Paula Vergara
    • 1
  • Javier Sedano
    • 2
  • Álvaro Herrero
    • 3
  1. 1.University of OviedoOviedoSpain
  2. 2.Instituto Tecnológico de Castilla y LeónBurgosSpain
  3. 3.Department of Civil EngineeringUniversity of BurgosBurgosSpain

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