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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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