Bankruptcy Prediction with Optuna-Enhanced Ensemble Machine Learning Methods: A Comparison of Oversampling and Undersampling Techniques

Abstract

Bankruptcy prediction is an essential task in financial risk management, often hindered by challenges such as class imbalance, feature selection, and overfitting. This study investigates the comparative effectiveness of data balancing techniques, specifically focusing on oversampling with SMOTE (Synthetic Minority Over-sampling Technique) and undersampling with Tomek Links, in addressing class imbalance in bankruptcy datasets. A range of machine learning models, including ensemble and boosting algorithms such as Stacking Classifier and XGBoost, were applied to imbalanced, SMOTE-balanced, and Tomek Links-balanced datasets. Dimensionality reduction was performed using Principal Component Analysis (PCA) to enhance computational efficiency and reduce overfitting risks, while hyperparameter optimization was conducted using the Optuna framework to maximize model performance. The findings demonstrate that SMOTE significantly improved classification accuracy and F1 scores, particularly for ensemble-based models, by generating synthetic samples to balance the dataset. In contrast, Tomek Links often reduced model performance due to the removal of potentially informative data points. Among the models tested, the Stacking Classifier performed best on SMOTE-balanced data, achieving a prediction accuracy of 99%. These results support integrating advanced predictive tools into financial decision-making. The Stacking Classifier’s strong performance on SMOTE-balanced data enhances risk management systems, enabling proactive bankruptcy detection.

Keywords

• Bankruptcy prediction
• data balancing techniques
• SMOTE
• ensemble machine learning
• Optuna

Optuna Destekli Topluluk Makine Öğrenmesi Yöntemleri ile İflas Tahmini: Aşırı ve Alt Örnekleme Tekniklerinin Karşılaştırılması

Abstract

İflas tahmini, finansal risk yönetiminin kritik bir bileşenidir; ancak bu süreç, sınıf dengesizliği, özellik seçimi ve aşırı öğrenme gibi önemli zorluklarla karşılaşmaktadır. Bu çalışmada, veri setlerindeki sınıf dengesizliği sorununu ele almak amacıyla kullanılan veri dengeleme tekniklerinin karşılaştırmalı bir analizi yapılmıştır. Çalışmada kullanılan veri seti, Taiwan Ekonomik Dergisi’nden alınmış olup, şirketlerin finansal kayıtlarını içermektedir. Çalışmada, Stacking Classifier ve XGBoost gibi topluluk ve yükseltme algoritmalarını içeren bir dizi makine öğrenmesi modeli, SMOTE ile Tomek Links kullanılarak dengelenmiş veri setlerinde test edilmiştir. Ayrıca, hesaplama verimliliğini artırmak ve aşırı öğrenme risklerini azaltmak amacıyla Temel Bileşen Analizi kullanılarak boyut indirgeme işlemi gerçekleştirilmiştir. Model performansını en üst düzeye çıkarmak için Optuna yöntemi ile hiperparametre optimizasyonu yapılmıştır. Elde edilen sonuçlar, SMOTE'un, özellikle topluluk tabanlı modellerde, veri setini dengeleyerek sınıflandırma doğruluğunu ve F1 skorlarını önemli ölçüde artırdığını göstermektedir. Buna karşın, Tomek Links yöntemi, bazı durumlarda potansiyel olarak önemli veri noktalarını kaldırdığı için model performansını olumsuz etkilemiştir. Test edilen modeller arasında, Stacking Classifier, SMOTE ile dengelenmiş veri setinde en iyi performansı sergileyerek %99 doğruluk oranına ulaşmıştır. Bu sonuçlar, gelişmiş tahmin araçlarının finansal karar alma süreçlerine entegrasyonunu desteklemektedir. Stacking Classifier’ın SMOTE ile dengelenmiş verilerdeki güçlü performansı, risk yönetim sistemlerini geliştirerek proaktif iflas tespitini mümkün kılmaktadır.

Keywords

• İflas tahmini
• veri dengeleme teknikleri
• SMOTE
• topluluk makine öğrenmesi
• Optuna

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