数据采集与处理2026，Vol.41Issue(3)：767-779,13.DOI:10.16337/j.1004-9037.2026.03.011
基于数据相似性和模型可靠度驱动的自适应模型融合方法
Adaptive Model Fusion Framework Driven by Data Similarity and Model Reliability
摘要
Abstract
Adaptive model fusion is particularly important for dynamically responding to the evolutionary characteristics of data and tasks.However,existing model fusion methods still have issues such as static weights being difficult to adapt to data similarity,dynamic fusion being driven by single factors,and being susceptible to data distribution drift.To address these shortcomings,this paper proposes an adaptive model fusion method driven by data similarity and model reliability.The method captures the similarity between samples through feature semantic alignment to obtain a similarity matrix,and further obtains the sample matching degree coefficient.Then,based on the base model selection algorithm of performance-diversity,the generalization ability and local performance of the base models are evaluated through multi-dimensional metrics to obtain the reliability coefficient of the base models.Finally,the fusion weight is calculated based on the data similarity coefficient and the reliability coefficient of the base models to obtain the final fusion model strategy.Experimental results on public datasets demonstrate the effectiveness of the proposed method.关键词
自适应模型融合/可靠性评估/样本匹配度/多维度度量/动态加权Key words
adaptive model fusion/reliability assessment/sample compatibility/multi-dimensional metrics/dynamic weighting分类
信息技术与安全科学引用本文复制引用
王梅,李艳培,高雅田..基于数据相似性和模型可靠度驱动的自适应模型融合方法[J].数据采集与处理,2026,41(3):767-779,13.基金项目
国家自然科学基金(51774090,62076234) （51774090,62076234）
黑龙江省科技创新基地项目(JD24A009) （JD24A009）
黑龙江省自然科学基金(LH2024F005) （LH2024F005）
黑龙江省博士后科研启动基金(LBH-Q20080). National Natural Science Foundation of China(Nos.51774090,62076234) （LBH-Q20080）
Heilongjiang Science and Technology Innovation Base Project(No.JD24A009) （No.JD24A009）
Heilongjiang Natural Science Foundation(No.LH2024F005) （No.LH2024F005）
Heilongjiang Postdoctoral Re-search Start-Up Fund(No.LBH-Q20080). （No.LBH-Q20080）
