基于朴素贝叶斯集成的正类-无标签学习算法OA

Existing ensemble-based positive and unlabeled learning(PUL)methods often fail to adequately account for the impact of noise in both positive and unlabeled samples during classifier construction.To address this issue,this paper proposes a naïve Bayesian ensemble-based PUL algorithm(NBEB-PUL),which consists of two stages:label assignment and noise filtering.In the label assignment stage,NBEB-PUL employs naïve Bayesian classifiers as base learners and integrates them using the AdaBoosting ensemble strategy to construct a strong classifier,named Ada-NBC.This strong classifier is then utilized to compute the mean posterior probabilities of a validation set for unlabeled samples,enabling explicit modeling of labeling uncertainty.Based on low-uncertainty predictions a set of reliable positive samples is iteratively identified,along with a residual unlabeled sample set.In the noise filtering stage,NBEB-PUL leverages the ensemble classifier generated in the first stage to reclassify the reliable positive set and the residual unlabeled set,resulting in Ada-NBC-predicted positive and negative sample sets.These sets are then intersected with the first-stage positive and unlabeled sets to extract overlapping high-confidence samples,forming the final positive and negative sample sets.The samples pruned from the dataset during this process are identified as noise.The feasibility,rationality,and effectiveness of NBEB-PUL were validated on 23 benchmark datasets from UCI and KEEL.Experimental results demonstrate that the algorithm exhibits stable convergence during the training process as the number of iterations increases.Moreover,NBEB-PUL outperforms six state-of-the-art PUL algorithms(S-EM,Biased-SVM,Modified-PUL,PU-LP,LP-PUL,and AdaPU)in terms of classification accuracy under varying positive sample proportions of 0.45,0.40,0.35,and 0.30.These results confirm that NBEB-PUL provides an effective and robust solution for positive and unlabeled learning in the presence of noise.