[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2045":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":22,"paper":23,"created_at":40},2045,"Adaptive Ensemble Machine Learning Framework for Robust Prediction, Classification, and Intelligent Decision Support Across Heterogeneous Data Domains","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.15911","The increasing diversity and complexity of data across different application domains create significant challenges for conventional machine learning models in achieving robust prediction, classification, and intelligent decision support.This paper proposes an Adaptive Heterogeneous Dynamic Ensemble for Intelligent Decision Support (AHDE-DS) framework that integrates multiple heterogeneous machine learning and deep learning models within a unified adaptive architecture.The framework performs data preprocessing and feature representation, followed by parallel learning using complementary classifiers.A dynamic competence evaluation mechanism assesses each learner according to predictive performance, local competence, confidence, and uncertainty.Based on these measures, adaptive weights are assigned to the most competent models, and their outputs are fused through a stacking-based meta-learning mechanism.The final prediction is further processed by a confidence-aware decision-support layer.Experimental evaluation demonstrates that AHDE-DS achieves 98.73% accuracy, 98.61% precision, 98.54% recall, and 98.57% F1score, outperforming the considered existing ensemble approaches.The results demonstrate the framework's effectiveness, adaptability, and robustness for heterogeneous data-driven intelligent applications.","不同应用领域数据的日益多样化和复杂化，给传统机器学习模型实现稳健的预测、分类和智能决策支持带来了重大挑战。本文提出了一种面向智能决策支持的自适应异构动态集成框架（AHDE-DS），该框架在统一的自适应架构中集成了多种异构机器学习和深度学习模型。该框架首先进行数据预处理和特征表示，随后利用互补分类器进行并行学习。一种动态能力评估机制根据预测性能、局部能力、置信度和不确定性对每个学习器进行评估。基于这些度量，为最具能力的模型分配自适应权重，并通过基于堆叠的元学习机制融合其输出。最终预测结果进一步由置信度感知的决策支持层进行处理。实验评估表明，AHDE-DS达到了98.73%的准确率、98.61%的精确率、98.54%的召回率和98.57%的F1分数，优于所考虑的现有集成方法。结果表明，该框架对于异构数据驱动的智能应用具有有效性、适应性和稳健性。",null,"IJARCCE","2026-09-09T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"通用机器学习集成框架论文，未涉及农业场景与数据，与三农及农业信息化无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21],"农业人工智能","10.17148\u002Fijarcce.2026.15911",{"doi":22,"openalex_id":24,"authors":25,"venue":10,"cited_by_count":15,"oa_url":6,"card":32,"direction":38,"ingested_from":39},"W7212075043",[26,28,30],{"name":27,"orcid":9},"M. Sasikumar",{"name":29,"orcid":9},"Addepalli Keerthika",{"name":31,"orcid":9},"N Saranya",{"tldr":33,"method":34,"finding":35,"direction":36,"opportunity":37},"提出自适应异构动态集成框架AHDE-DS，实现跨异构数据的鲁棒预测、分类与智能决策支持。","融合多种机器学习与深度学习模型，采用动态能力评估、自适应加权与堆叠元学习。","实验准确率达98.73%，优于现有集成方法，验证了框架的适应性与鲁棒性。","农业人工智能与决策模型","可将该自适应集成框架迁移至农业多源异构数据（气象、土壤、遥感）的病害预警与决策场景。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.623326Z"]