Abstract: A Weighted NaÏve Bayesian Ensemble Classification（WEBNC） algorithm based on correlation degree of attributes is proposed to improve the classification performance of classifiers.A weight is set to each attribute according to its correlation degree with the decision attribute，and the training data with weighted attributes are sampled to learn member classifiers.The algorithm is tested on 16 UCI datasets，and compared with NaÏve Bayesian Classifier（BNC），BNC net and BNC trained based on AdaBoost.The results illustrate the ensemble classifier improves the classification performance.

摘要： 为提高朴素贝叶斯分类器的分类精度和泛化能力，提出了基于属性相关性的加权贝叶斯集成方法（WEBNC）。根据每个条件属性与决策属性的相关度对其赋以相应的权值，然后用AdaBoost训练属性加权后的BNC。该分类方法在16个UCI标准数据集上进行了测试，并与BNC、贝叶斯网和由AdaBoost训练出的BNC进行比较，实验结果表明，该分类器具有更高的分类精度与泛化能力。