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OA

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.