<![CDATA[Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that favor the majority class. This issue is especially pronounced in the classification of financial distress, where data imbalance is common due to the scarcity of such instances in real-world datasets. This study aims to mitigate data imbalance in financial distress companies using the Kmeans-SMOTE method by combining Kmeans clustering and the synthetic minority oversampling technique (SMOTE). Various classification approaches, including Nave Bayes and support vector machine (SVM), are implemented on a Kaggle financial distress data set to evaluate the effectiveness of Kmeans-SMOTE. Experimental results show that SVM outperforms Nave Bayes with impressive accuracy (99.1%), f1-score (99.1%), area under precision recall (AUPRC) (99.1%), and geometric mean (Gmean) (98.1%). On the basis of these results, Kmeans-SMOTE can balance the data effectively, leading to a quite significant improvement in performance.]]>
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