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Class distribution disparities in datasets often result in imbalanced data issues, which can significantly impact model performance. This study investigates the effects of such imbalances on the performance of XGBoost and Support Vector Machines (SVM), specifically in the context of a five-class classification problem using the financial freedom index as the target variable. Initially, both models were applied to the imbalanced dataset, highlighting the performance degradation caused by the data imbalance. To mitigate this issue, the Synthetic Minority Oversampling Technique (SMOTE) was employed to generate a balanced dataset, after which the models were re-evaluated. Comparative analysis revealed that the XGBoost algorithm demonstrated superior performance relative to the SVM method once the data imbalance was addressed. Moreover, the improvement in classification accuracy for XGBoost was notably higher compared to SVM following the application of the SMOTE technique, underscoring the robustness of XGBoost in handling imbalanced data.

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The Imbalanced Data Problem: Investigating Factors Affecting Financial Freedom Using Data Mining Techniques with SMOTE Method

  • 章节

  • 发表日期：

    2025年

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  • Abdurrahman Coşkuner,
  • Ömer Faruk Rençber

摘要

Class distribution disparities in datasets often result in imbalanced data issues, which can significantly impact model performance. This study investigates the effects of such imbalances on the performance of XGBoost and Support Vector Machines (SVM), specifically in the context of a five-class classification problem using the financial freedom index as the target variable. Initially, both models were applied to the imbalanced dataset, highlighting the performance degradation caused by the data imbalance. To mitigate this issue, the Synthetic Minority Oversampling Technique (SMOTE) was employed to generate a balanced dataset, after which the models were re-evaluated. Comparative analysis revealed that the XGBoost algorithm demonstrated superior performance relative to the SVM method once the data imbalance was addressed. Moreover, the improvement in classification accuracy for XGBoost was notably higher compared to SVM following the application of the SMOTE technique, underscoring the robustness of XGBoost in handling imbalanced data.