Ensembles of Multiobjective-Based Classifiers for Detection of Epileptic Seizures

  • Fernando S. Beserra
  • Marcos M. Raimundo
  • Fernando J. Von Zuben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


This paper proposes multiobjective-based classifiers to detect epileptic seizures using ensemble approaches, transfer-learning methods, and three alternative feature extraction techniques. Two aspects of the problem were investigated: (1) the relative merit of distinct proposals to synthesize an ensemble of classifiers, considering all the three feature extraction techniques; (2) the potential of an ensemble composed of transfer-learned classifiers. The blend approaches with the best performance detected all test seizures, with a high proportion of correctly detected samples inside the seizure interval and high proportion of time intervals correctly classified as non-seizures.


Ensemble of classifiers Transfer learning Multiobjective optimization Epilepsy 



This research was supported by FAPESP, processes 16/19080-2 and 14/13533-0, and by CNPq, process 309115/2014-0.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fernando S. Beserra
    • 1
  • Marcos M. Raimundo
    • 1
  • Fernando J. Von Zuben
    • 1
  1. 1.LBiC/DCA/FEEC - University of CampinasCampinasBrazil

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