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<p>There is a growing need for labeled data, yet manual annotation is costly, error-prone, and often infeasible in privacy-sensitive, highly imbalanced domains such as fraud detection. We introduce a fully unsupervised framework that combines unsupervised SHapley Additive exPlanations (SHAP) feature selection with our novel unsupervised labeling method. We apply unsupervised SHAP to the Kaggle Credit Card Fraud Detection and Medicare Part D datasets to produce high-impact feature subsets, and then label the datasets with our unsupervised labeling approach. To effectively evaluate the labels generated by our novel methodology, we apply a baseline unsupervised learner, Isolation Forest (IF), to both the original datasets and their subsets. We calculate Matthew’s Correlation Coefficient (MCC), Jaccard Index (JI), Precision, Recall, and F1-score by comparing our generated labels against the ground truth labels. It is important to note, the ground truth labels were used solely for evaluation. Our empirical results surpass the results obtained with the full feature dataset and baseline. By improving label quality while reducing computational complexity and preserving privacy, our approach offers a practical solution for learning from unlabeled, severely imbalanced data.</p>

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Scalable unsupervised labeling with SHAP feature selection for fraud detection in imbalanced data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Mary Anne Walauskis,
  • Taghi M. Khoshgoftaar

摘要

There is a growing need for labeled data, yet manual annotation is costly, error-prone, and often infeasible in privacy-sensitive, highly imbalanced domains such as fraud detection. We introduce a fully unsupervised framework that combines unsupervised SHapley Additive exPlanations (SHAP) feature selection with our novel unsupervised labeling method. We apply unsupervised SHAP to the Kaggle Credit Card Fraud Detection and Medicare Part D datasets to produce high-impact feature subsets, and then label the datasets with our unsupervised labeling approach. To effectively evaluate the labels generated by our novel methodology, we apply a baseline unsupervised learner, Isolation Forest (IF), to both the original datasets and their subsets. We calculate Matthew’s Correlation Coefficient (MCC), Jaccard Index (JI), Precision, Recall, and F1-score by comparing our generated labels against the ground truth labels. It is important to note, the ground truth labels were used solely for evaluation. Our empirical results surpass the results obtained with the full feature dataset and baseline. By improving label quality while reducing computational complexity and preserving privacy, our approach offers a practical solution for learning from unlabeled, severely imbalanced data.