Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data
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Publisher
AIP Publishing
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0128413
Reference23 articles.
1. Cen L, 2007 Classifying imbalanced data using a Bagging Ensemble Variation (BEV) Proc. Annu. Southeast Conf. 2007 p. 203–208.
2. Siringoringo R. 2018. Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan k-Nearest Neighbor J. ISD 3, 1 p. 44–49.
3. Muqiit W.S. A. and Nooraeni R. 2020. Penerapan Metode Resampling Dalam Mengatasi Imbalanced Data Pada Determinan Kasus Diare Pada Balita Di Indonesia (Analisis Data Sdki 2017) J. MSA (Mat. dan Stat. serta Apl.) 8, 1 p. 19.
4. Laopracha N., Sunat K. and Chiewchanwattana S. 2019. A Novel Feature Selection in Vehicle Detection Through the Selection of Dominant Patterns of Histograms of Oriented Gradients (DPHOG) IEEE Access 7 p. 20894– 2019.
5. Han D., Member S., and Kim J. 2018. Unified Simultaneous Clustering and Feature Selection for Unlabeled and Labeled Data p. 1–16.
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