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<p>Legal outcome prediction is a complex task due to the nuanced vocabulary and reasoning embedded in judicial documents. This study proposes LABDT (Lexi AdaBoost Decision Tree), a novel hybrid framework that integrates lexical features (TF-IDF), semantic embeddings (Sentence-BERT), and similarity-based metrics for robust and interpretable legal decision classification. The model addresses class imbalance through SMOTE and reduces feature dimensionality via principal component analysis (PCA). LABDT was evaluated on a real-world dataset of approximately 18,000 cases from the Federal Court of Australia case records, spanning eight outcome classes. The results demonstrate that LABDT outperforms traditional and state-of-the-art classifiers with an accuracy of 92%, precision of 91%, recall of 91%, and F1-score of 91%. Notably, it achieved highly accurate classification on certain outcome classes like ‘approved’ and ‘related’. LABDT offers a superior balance between predictive performance and model interpretability compared to baseline models. The system’s explainable design and high classification reliability position it as a viable tool for AI-driven legal analytics and judicial decision support.</p>

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LABDT (Lexi AdaBoost Decision Tree): An Approach for Legal Outcome Prediction Fusing Lexical, Semantic, and Similarity-based features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Sanjay Kumar Sahoo,
  • Avinash Samantra,
  • Chinmaya Kumar Mohapatra,
  • Bishnu Prasad Swain,
  • Zulfiqar Ali,
  • Ghulam Muhammad

摘要

Legal outcome prediction is a complex task due to the nuanced vocabulary and reasoning embedded in judicial documents. This study proposes LABDT (Lexi AdaBoost Decision Tree), a novel hybrid framework that integrates lexical features (TF-IDF), semantic embeddings (Sentence-BERT), and similarity-based metrics for robust and interpretable legal decision classification. The model addresses class imbalance through SMOTE and reduces feature dimensionality via principal component analysis (PCA). LABDT was evaluated on a real-world dataset of approximately 18,000 cases from the Federal Court of Australia case records, spanning eight outcome classes. The results demonstrate that LABDT outperforms traditional and state-of-the-art classifiers with an accuracy of 92%, precision of 91%, recall of 91%, and F1-score of 91%. Notably, it achieved highly accurate classification on certain outcome classes like ‘approved’ and ‘related’. LABDT offers a superior balance between predictive performance and model interpretability compared to baseline models. The system’s explainable design and high classification reliability position it as a viable tool for AI-driven legal analytics and judicial decision support.