基于实例硬度加权采样的增强Bagging集成分类方法OA

Ensemble learning is a class of machine learning methods that make decisions by constructing and combining the outputs of multiple base classifiers.Among these,the representative Bagging ensemble method is frequently utilized for classification,regression,modeling in noisy environments,and learning from imbalanced data.Addressing the issues of unstable classification performance and insufficient focus on hard-to-classify samples inherent in traditional Bagging methods when applied to noisy datasets,a new en-semble learning method named eNewBagging is proposed,which integrates Instance Hardness(IH)with an enhanced bootstrapping mechanism.Based on the eBagging framework,an IH-based weighted sampling strat-egy is introduced.By allocating adaptive weights to samples,this method suppresses the influence of noisy samples and intensifies the learning of hard-to-classify samples,thereby improving overall accuracy and ro-bustness while maintaining classifier diversity.Experiments are conducted on seven public datasets from UCI and KEEL.Evaluations on the original data are performed using two base classifiers:k-Nearest Neighbors(kNN)and Decision Trees(DT).Furthermore,performance comparisons are conducted exclusively using the kNN base classifier under varying noise level ratios ranging from 2％to 30％.The results indicate that eN-ewBagging can achieve optimal or sub-optimal performance across the ACC,AUC,and F1 metrics.In com-parison with BaggingIH and GrpMixBag,eNewBagging exhibits a smaller margin of decline on highly noisy datasets,demonstrating stronger noise robustness.While preserving the bias-variance balance,this method achieves enhanced learning of borderline samples,significantly elevating the model's generalization perfor-mance in complex data environments.It is expected that the eNewBagging method can provide an efficient framework to overcome the performance degradation of ensemble learning caused by noise interference and sample complexity.