PERFORMANCE COMPARISON OF SMOTE-BASED MACHINE LEARNING MODELS ON UNBALANCED DATASETS: A STUDY ON DATE AND PISTACHIO FRUITS

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dc.contributor.authorBal, Fatih
dc.contributor.authorKayaalp, Fatih
dc.date.accessioned2025-10-11T20:37:53Z
dc.date.available2025-10-11T20:37:53Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractCreating balanced datasets is a significant challenge that substantially affects the performance of machine learning models in the classification of agricultural products. In this research, we tried to overcome this challenge by using an unbalanced dataset containing information on 7 date palm (Phoenix dactylifera L.) and 2 pistachio (Pistacia vera L.) cultivars. The aim of the study is to compare the classification performance of machine learning models on an unbalanced dataset and a balanced dataset using the SMOTE technique. Initially, classification was performed on the unbalanced dataset using machine learning approaches. Among the machine learning models applied on the unbalanced dataset, the Linear-SVM model showed the highest accuracy rate with an accuracy rate of 92,62%. In the data set extended by applying the SMOTE technique, the RBF-SVM model again showed the highest accuracy rate with 95,55% accuracy rate. In summary, our study highlights the difficulties in machine learning-based agricultural crop classification due to data unbalances. Utilizing the SMOTE technique for oversampling was effective in overcoming this obstacle and improving classification accuracy.en_US
dc.identifier.doi10.55071/ticaretfbd.1597150
dc.identifier.endpage200en_US
dc.identifier.issn1305-7820
dc.identifier.issn2587-165X
dc.identifier.issue47en_US
dc.identifier.startpage176en_US
dc.identifier.trdizinid1319300en_US
dc.identifier.urihttps://doi.org/10.55071/ticaretfbd.1597150
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1319300
dc.identifier.urihttps://hdl.handle.net/20.500.12684/20733
dc.identifier.volume24en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofİstanbul Ticaret Üniversitesi Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250911
dc.subjectSMOTEen_US
dc.subjectMachine learningen_US
dc.subjectfruit classificationen_US
dc.subjectoversamplingen_US
dc.titlePERFORMANCE COMPARISON OF SMOTE-BASED MACHINE LEARNING MODELS ON UNBALANCED DATASETS: A STUDY ON DATE AND PISTACHIO FRUITSen_US
dc.typeArticleen_US

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