Performance of Random Oversampling, Random Undersampling, and SMOTE-NC Methods in Handling Imbalanced Class in Classification Models
Year: 2024
Type: article
Abstract: 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).
Author: Andika Putri Ratnasari
Institution: Yogyakarta State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4396496548