Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset

Meida Cahyo Untoro; Mugi Praseptiawan; Mastuti Widianingsih; Ilham Firman Ashari; Aidil Afriansyah; Oktafianto

doi:10.1088/1742-6596/1477/3/032005

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Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset

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Meida Cahyo Untoro, Mugi Praseptiawan, Mastuti Widianingsih, Ilham Firman Ashari, Aidil Afriansyah, Oktafianto

2020 Journal of Physics: Conference Series Vol. 1477 Issue 3 Conference paper Cited by 13 SDG 4SDG 16

Abstract

Imbalanced data causes misclassification because the majority of the dominant data is in the minority data, which results in a decrease in the value of accuracy. UCI dataset is a public dataset that can be used as a dataset in machine learning. This study aims to evaluate the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods on data imbalances in MWMOTE. MWMOTE is used in resolving Imbalanced cases through weighting and grouping. This goal is achieved by evaluating the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods in MWMOTE to produce more representative synthetic data and increase the accuracy value. The results obtained from this study indicate that the Decision Tree has higher evaluations of recall, precision, F-measure, and accuracy compared to K-NN, Naive Bayes, and Support Vector Machine for data that are balanced with MWMOTE. © 2020 IOP Publishing Ltd. All rights reserved.

Affiliations

Department of Informatics Engineering, Institut Teknologi Sumatera, Indonesia; Department of Biology, Universitas Gadjah Mada, Indonesia

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