面向Stacking集成的改进分类算法及其应用

为了提高Stacking集成算法的分类性能,充分利用Stacking学习机制产生的先验信息和贝叶斯网络丰富的概率表达能力,提出一种基于属性值加权朴素贝叶斯算法的Stacking集成分类算法AVWNB-Stacking(Stac-king based Attribute Value Weight Naive Bayes)...

IMPROVED CLASSIFICATION ALGORITHM FOR STACKING INTEGRATION AND ITS APPLICATION

In order to improve the classification performance of the Stacking integration algorithm,making full use of the a priori information generated by learning mechanism of Stacking and the rich probability expression ability of the Bayesian network,a Stacking integrated classification algorithm based on attribute value weighted Naive Bayes algorithm,AVWNB-Stacking(Stacking based Attribute Value Weight Naive Bayes),is proposed.By considering the deep factor of attribute values and using mutual information(MI)as a basis of weight measure,we expanded horizontally the attribute weight vector and assigned a weight to each attribute value,avoiding different attribute values sharing the same weight value,thereby solving loss of classification accuracy brought by the Naive Bayes algorithm as a Stacking meta classifier due to attribute independence assumptions.The experimental results show that compared with the traditional algorithms and other meta-classifiers Stacking classification algorithm,the AVWNB-Stacking algorithm effectively improves the classification performance of the model,and the AUC value reaches 0.8007 and 0.8607 on the two test sets respectively.