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.