Comparative Study of SMOTE and Bootstrapping Performance Based on Predication Methods

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dc.cclicenceN/Aen
dc.contributor.authorAborujilah, Abdulaziz
dc.contributor.authorNassr, Rasheed Mohammad
dc.contributor.authorAl-Hadhrami, Tawfik
dc.contributor.authorHusen, Mohd Nizam
dc.contributor.authorAli, Nor Azlina
dc.contributor.authorAl-Othmani, Abdulaleem
dc.contributor.authorHamdi, Mustapha
dc.date.acceptance2021
dc.date.accessioned2023-05-11T08:35:31Z
dc.date.available2023-05-11T08:35:31Z
dc.date.issued2021-05-06
dc.description.abstractRecently, there has been a renewed interest in smart health systems that aim to deliver high quality healthcare services. Prediction methods are very essential to support these systems. They mainly rely on datasets with assumptions that match the reality. However, one of the greatest challenges to prediction methods is to have datasets which are normally distributed. This paper presents an experimental work to implement SMOTE (Synthetic Minority Oversampling Technique) and bootstrapping methods to normalize datasets. It also measured the impact of both methods in the performance of different prediction methods such as Support vector machine (SVM), Naive Bayes, and neural network(NN) The results showed that bootstrapping with native bays yielded better prediction performance as compared to other prediction methods with SMOTE.en
dc.funderNo external funderen
dc.identifier.citationAborujilah, A., Nassr, R.M., Al-Hadhrami, T., Husen, M.N., Ali, N.A., Othmani, A.A. and Hamdi, M. (2021) Comparative study of SMOTE and bootstrapping performance based on predication methods. In: Innovative Systems for Intelligent Health Informatics: Data Science, Health Informatics, Intelligent Systems, Smart Computing (pp. 3-9). Cham: Springer International Publishing.en
dc.identifier.doihttps://doi.org/10.1007/978-3-030-70713-2_1
dc.identifier.issn2367-4512
dc.identifier.urihttps://hdl.handle.net/2086/22914
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture Notes on Data Engineering and Communications Technologies;volume 72, pp. 3-9
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectDatasets normalizationen
dc.subjectPrediction systemsen
dc.subjectDataset redistribution methodsen
dc.subjectSMOTE-Bootstrappingen
dc.titleComparative Study of SMOTE and Bootstrapping Performance Based on Predication Methodsen
dc.typeBook chapteren

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