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<p>The surge in biomedical literature and clinical reports presents a formidable challenge for automated text analysis, particularly in multi-label classification tasks where severe class imbalance and interdependent labels are common. To address these issues, we propose MITHEM (Multi-label Imbalance-aware Text Classification using Hybrid Ensemble Model), an ensemble framework that combines threshold-guided binning, SMOTE based oversampling, and a set of diverse classifiers Support Vector Machines, Decision Trees, and Random Forests within a meta-classification approach. Unlike traditional techniques, MITHEM not only improves the representation of minority classes but also learns correlations between labels to refine decision-making. We tested the framework on eight standard biomedical text datasets and observed notable gains in macro F1-score, Hamming loss, and label coverage compared with strong baselines. Empirical results validate that MITHEM outperforms other competitive methods on a variety of biomedical datasets, especially in imbalanced scenarios, in terms of enhanced Recall and Precision scores.</p>

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Multi-label imbalanced text handling using ensemble methodology with application to biomedical data classification

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Subhajit Ghosh,
  • Sanidhya Gupta,
  • Sourav Bhattacharyya,
  • Avik Kumar Das,
  • Apurba Nandi,
  • Ardhendu Sarkar,
  • Partha Sarathi Samanta,
  • Chantri Polprasert

摘要

The surge in biomedical literature and clinical reports presents a formidable challenge for automated text analysis, particularly in multi-label classification tasks where severe class imbalance and interdependent labels are common. To address these issues, we propose MITHEM (Multi-label Imbalance-aware Text Classification using Hybrid Ensemble Model), an ensemble framework that combines threshold-guided binning, SMOTE based oversampling, and a set of diverse classifiers Support Vector Machines, Decision Trees, and Random Forests within a meta-classification approach. Unlike traditional techniques, MITHEM not only improves the representation of minority classes but also learns correlations between labels to refine decision-making. We tested the framework on eight standard biomedical text datasets and observed notable gains in macro F1-score, Hamming loss, and label coverage compared with strong baselines. Empirical results validate that MITHEM outperforms other competitive methods on a variety of biomedical datasets, especially in imbalanced scenarios, in terms of enhanced Recall and Precision scores.