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dc.contributor.authorGameng, Hazel A.
dc.contributor.authorGerardo, Bobby D.
dc.contributor.authorMedina, Ruji P.
dc.date.accessioned2024-08-16T06:35:04Z
dc.date.available2024-08-16T06:35:04Z
dc.date.issued2020-06-16
dc.identifier.citationGameng, H. A., Gerardo, B. B., & Medina, R. P. (2019). Modified adaptive synthetic SMOTE to improve classification performance in imbalanced datasets. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-5). Kuala Lumpur, Malaysia: IEEE.en
dc.identifier.isbn978-1-7281-4082-7
dc.identifier.urihttps://hdl.handle.net/20.500.14353/609
dc.description.abstractThe oversampling technique in the data preprocessing has been utilized to mitigate the imbalanced data problem in the real research scenario. This imbalance may reduce the ability of classification algorithms to recognize cases of interest leading to misclassification of positive samples as negative class or the false positive generation. Synthetic Minority Oversampling Technique (SMOTE) is one of the oversampling techniques existing and the Adaptive Synthetic (Adasyn) SMOTE is one of its many variants. K-Nearest Neighbor (KNN) is incorporated in Adasyn. In this study, Manhattan distance is applied in the KNN computations. This modified Adasyn was evaluated in terms of its effectiveness in the performance measure of overall accuracy, precision, recall and F1 measure on the six imbalanced datasets using logistic regression as the classification algorithm. The modified Adasyn dominated over SMOTE and the original Adasyn by 66.67 percent of the total performance metric count. It leads the accuracy and recall count with 4 out of 6, precision count with 3 out of 6, and the F1 measure count with 5 over 6. Thus, proving that the modified Adasyn can provide an efficient solution in decreasing misclassification on imbalanced datasets.en
dc.description.sponsorshipAcknowledgment: The authors are appreciative to the reviewers for the positive remarks and suggestions. This effort is supported by Philippine Government through the Commission on Higher Education. The authors are grateful for the financial provision provided on this study.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.subjectAdaptive Synthetic SMOTEen
dc.subjectClassificationen
dc.subjectImbalanced dataseten
dc.subjectManhattan Distanceen
dc.subjectSMOTEen
dc.subjectClassification performanceen
dc.subjectADASYNen
dc.subjectOversampling methodsen
dc.subject.lcshLogistic regression analysisen
dc.subject.lcshNearest neighbor analysis (Statistics)en
dc.subject.lcshAlgorithmsen
dc.subject.lcshInformation storage and retrieval systems--Classificationen
dc.subject.lcshProblem solving--Data processingen
dc.titleModified adaptive synthetic SMOTE to improve classification performance in imbalanced datasetsen
dc.typeConference paperen
dcterms.accessRightsLimited public accessen
dc.citation.firstpage1en
dc.citation.lastpage5en
dc.identifier.doi10.1109/ICETAS48360.2019.9117287
dc.citation.conferencetitle6th IEEE International Conference on Engineering, Technologies and Applied Sciences, ICETAS 2019en
local.isIndexedByScopusen
local.subject.agrovocalgorithmsen
local.subject.agrovocdata processingen
local.subject.agrovocclassification systemsen


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