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ISSN : 2321-3418
 1. Home /
 2. Archives /
 3. Vol. 12 No. 04 (2024) /
 4. Mathematics and Statistics


PERFORMANCE OF RANDOM OVERSAMPLING, RANDOM UNDERSAMPLING, AND SMOTE-NC METHODS
IN HANDLING IMBALANCED CLASS IN CLASSIFICATION MODELS

https://doi.org/10.18535/ijsrm/v12i04.m03



AUTHORS

 * Andika Putri Ratnasari Universitas Negeri Yogyakarta, Faculty of Mathematics
   and Natural Sciences, Colombo Road, Yogyakarta, , Indonesia

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One common challenge in classification modeling is the existence of imbalanced
classes within the data. If the analysis continues with imbalanced classes, it
is probable that the result will demonstrate inadequate performance when
forecasting new data. Various approaches exist to rectify this class imbalance
issue, such as random oversampling, random undersampling, and the Synthetic
Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC). Each of
these methods encompasses distinct techniques aimed at achieving balanced class
distribution within the dataset. Comparison of classification performance on
imbalanced classes handled by these three methods has never been carried out in
previous research. Therefore, this study undertakes an evaluation of
classification models (specifically Gradient Boosting, Random Forest, and
Extremely Randomized Trees) in the context of imbalanced class data. The results
of this research show that the random undersampling method used to balance the
class distribution has the best performance on two classification models (Random
Forest and Gradient Boosted Tree).

Performance of Random Oversampling, Random Undersampling, and SMOTE-NC Methods
in Handling Imbalanced Class in Classification Models. (2024). International
Journal of Scientific Research and Management (IJSRM), 12(04), 494-501.
https://doi.org/10.18535/ijsrm/v12i04.m03
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Copyright (c) 2024 Andika Putri Ratnasari

This work is licensed under a Creative Commons Attribution 4.0 International
License.


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ISSUE

Vol. 12 No. 04 (2024)


SECTION

Mathematics and Statistics


PUBLISHED

April 29, 2024


KEYWORDS

Classification imbalanced class random oversampling random undersampling
SMOTE-NC

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