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


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

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

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. More... »

PAGES

439-448

References to SciGraph publications

Book

TITLE

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

ISBN

978-3-319-67179-6
978-3-319-67180-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67180-2_43

DOI

http://dx.doi.org/10.1007/978-3-319-67180-2_43

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091284900


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