Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data

Adli Abdillah Nababan; Sutarman Sutarman; Muhammad Zarlis; Erna Budhiarti Nababan

doi:10.1063/5.0128413

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Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data

Adli Abdillah Nababan Sutarman Sutarman Muhammad Zarlis Erna Budhiarti Nababan

Year: 2023 Journal: AIP conference proceedings Vol: 2714 Pages: 030012-030012 Publisher: American Institute of Physics

DOI: 10.1063/5.0128413

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Keywords:

Oversampling k-nearest neighbors algorithm Class (philosophy) Computer science Artificial intelligence Pattern recognition (psychology) Data mining Telecommunications Bandwidth (computing)

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Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

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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