Abstract: To screen and weight base classifiers with correlation during the ensemble process of multiple classifiers,this paper proposes a selective ensemble learning method for optimal similarity based on LLaMa3 and Choquet integrals(LCOS-SELM).Leveraging the open-source LLaMa3,this method efficiently achieves key feature extraction from unstructured text through prompt learning,using only a small amount of labeled data.Subsequently,it integrates the prediction results of classifiers with correlated relationships using the Choquet integral,evaluating their correlation to optimize classifier selection.Finally,it employs an optimal similarity strategy to learn classifier weights,ensuring sample consistency while enhancing the performance of the ensemble me-thod.The LCOS-SELM is used for the auxiliary diagnosis of Crohn’s disease,using 297 examination reports from a tertiary hospital in Hefei.Experiments are conducted by comparing it with endoscopic examination reports to validate the effectiveness of the proposed method.Under the same experimental conditions,LCOS-SELM demonstrates an approximate 8% improvement in Accuracy (Acc),F1-score,and AUC compared to the single classifier,and an approximate 2% improvement in all three metrics compared to traditional ensemble models,further validating its performance advantage.

Key words: Selective ensemble learning, LLaMa3, Choquet integral, Weight learning, Similar case learning