DOI: https://doi.org/10.14710/transmisi.22.3.102-106

FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION

*Farrikh Alzami scopus  -  Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia, Indonesia
Aries Jehan Tamamy  -  Faculty of Technic, Universitas Dian Nuswantoro, Semarang, Indonesia, Indonesia
Ricardus Anggi Pramunendar  -  Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia, Indonesia
Zaenal Arifin  -  Faculty of Technic, Universitas Dian Nuswantoro, Semarang, Indonesia, Indonesia
Dikirim: 5 Jan 2020; Diterbitkan: 17 Agu 2020.
Akses Terbuka Copyright (c) 2020 Transmisi under http://creativecommons.org/licenses/by-sa/4.0.

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The ensemble learning approach, especially in classification, has been widely carried out and is successful in many scopes, but unfortunately not many ensemble approaches are used for the detection and classification of epilepsy in biomedical terms. Compared to using a simple bagging ensemble framework, we propose a fusion bagging-based ensemble framework (FBEF) that uses 3 weak learners in each oracle, using fusion rules, a weak learner will give results as predictors of the oracle. All oracle predictors will be included in the trust factor to get a better prediction and classification. Compared to traditional Ensemble bagging and single learner type Ensemble bagging, our framework outperforms similar research in relation to the epileptic seizure classification as 98.11±0.68 and several real-world datasets
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Kata Kunci: Epileptic seizure detection; wavelet analysis; feature selection; ensemble; fusion; bagging;

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Bagian: Artikel - Biomedik
Bahasa : EN
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  1. . R. S. Fisher et al., “Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE).,” Epilepsia, vol. 46, no. 4, pp. 470–2, Apr. 2005
  2. . A. Shoeb and J. Guttag, “Application of Machine Learning to Epileptic Seizure Detection,” in Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp. 975–982
  3. . F. Alzami et al., “Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification,” IEEE Access, vol. 6, pp. 29132–29145, 2018
  4. . M. Li, W. Chen, and T. Zhang, “Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble,” Biomed. Signal Process. Control, vol. 31, pp. 357–365, Jan. 2017
  5. . N. S. Tawfik, S. M. Youssef, and M. Kholief, “A hybrid automated detection of epileptic seizures in EEG records,” Comput. Electr. Eng., vol. 53, pp. 177–190, Jul. 2016
  6. . F. Alzami, D. Wang, Z. Yu, J. You, H. S. Wong, and G. Han, “Robust epileptic seizure classification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9772, 2016, pp. 363–373
  7. . T. N. Alotaiby, S. A. Alshebeili, T. Alshawi, I. Ahmad, and F. E. Abd El-Samie, “EEG seizure detection and prediction algorithms: a survey,” EURASIP J. Adv. Signal Process., vol. 2014, no. 1, p. 183, Dec. 2014
  8. . Tin Kam Ho, “The random subspace method for constructing decision forests,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832–844, 1998
  9. . Zhiwen Yu, Le Li, Jiming Liu, and Guoqiang Han, “Hybrid Adaptive Classifier Ensemble,” IEEE Trans. Cybern., vol. 45, no. 2, pp. 177–190, Feb. 2015
  10. . Hanchuan Peng, Fuhui Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226–1238, Aug. 2005
  11. . L. Xu, A. Krzyzak, and C. Y. Suen, “Methods of combining multiple classifiers and their applications to handwriting recognition,” IEEE Trans. Syst. Man. Cybern., vol. 22, no. 3, pp. 418–435, 1992
  12. . L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms: Second Edition, 2nd Editio., vol. 9781118315. New Jersey: John Wiley & Sons, 2014
  13. . J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, “On combining classifiers,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226–239, Mar. 1998
  14. . R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E, vol. 64, no. 6, p. 061907, Nov. 2001
  15. . S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, “Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection,” IEEE Trans. Biomed. Eng., vol. 54, no. 9, pp. 1545–1551, Sep. 2007
  16. . I. Guler and E. D. Ubeyli, “Multiclass Support Vector Machines for EEG-Signals Classification,” IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 2, pp. 117–126, Mar. 2007
  17. . K.-C. Hsu and S.-N. Yu, “Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm,” Comput. Biol. Med., vol. 40, no. 10, pp. 823–830, Oct. 2010

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