南京理工大学学报（自然科学版）2024，Vol.48Issue(4)：479-488,10.DOI:10.14177/j.cnki.32-1397n.2024.48.04.009
散度加权的平均一阶依赖估计分类算法研究
Averaged one-dependence estimators classification algorithm based on divergence weighting
摘要
Abstract
The averaged one-dependence estimators(AODE)algorithm is an important extension of the naive Bayesian classification algorithm.However,AODE treats all attributes equally,which limits its ability of improving classification performance.In order to accurately characterize the role of each attribute in classification and further improve the classification performance of AODE,this paper proposes a divergence weighted AODE classification algorithm.The method introduces two divergence metrics,Kullback-Leibler divergence and Jessen-Shannon divergence,and uses them to determine the weights of the super parent one-dependence estimators in the AODE classification framework.These weights are based on the divergence between the prior distribution of the class variable and the posterior distribution of the given attribute values.The results are in a more optimal way of combining the super parent one-dependence estimators.Experiments on 36 data sets from the University of California machine learning repository show that the divergence weighted AODE algorithm significantly outperforms the original AODE algorithm.Consequently,the use of divergence weighting can effectively improve the classification performance of AODE.关键词
平均一阶依赖估计/Kullback-Leibler散度/Jessen-Shannon散度/加权Key words
averaged one-dependence estimators/Kullback-Leibler divergence/Jessen-Shannon divergence/weighting分类
信息技术与安全科学引用本文复制引用
陈圣磊,高兴宇,卓超,朱昌舰..散度加权的平均一阶依赖估计分类算法研究[J].南京理工大学学报（自然科学版）,2024,48(4):479-488,10.基金项目
国家自然科学基金(62276136) （62276136）
江苏省研究生科研与实践创新计划项目(SJCX23_1105) （SJCX23_1105）
