Explainable Ensemble Learning for Environmental, Social, Governance Score Prediction Through Rule Extraction and Pruning

Le résumé fourni par la source

Environmental, Social, and Governance (ESG) indicators have become critical determinants in sustainable investment and regulatory decision-making. However, building models that deliver both high predictive accuracy and interpretability remains a major challenge. This paper presents an explainable ensemble learning framework that enhances the interpretability of ESG score prediction through rule extraction and pruning. The proposed approach applies a structured preprocessing, feature encoding, and Random Forest-based ensemble learning. Further, decision rules are extracted and locally pruned to eliminate redundancy while preserving predictive power. The refined rules are aggregated using a weighted voting mechanism for multi-class ESG classification. Experimental evaluation on firm-level ESG datasets demonstrates that the proposed approach achieves competitive predictive accuracy (AUC$=0.90$, Precision$=0.72$) while producing concise, humaninterpretable rules. enhancing transparency and actionable insights for sustainable finance.

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