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
陈圣磊 1高兴宇 2卓超 2朱昌舰2
作者信息
- 1. 南京审计大学 经济学院,江苏 南京 211815
- 2. 南京审计大学 计算机学院,江苏 南京 211815
- 折叠
摘要
平均一阶依赖估计(AODE)是对朴素贝叶斯分类算法的重要扩展,然而,AODE平等地对待各个属性,这限制了其分类性能的提升.为了准确刻画各个属性对于分类的作用,进一步提升 AODE 的分类性能,该文提出一种基于散度加权的 AODE 分类算法.该方法引入了Kullback-Leibler散度和Jessen-Shannon散度2 种散度指标,基于类别的先验分布和给定属性取值的后验分布之间的散度,构建AODE分类框架中超级父属性一阶依赖估计器的权值,从而得到超级父属性一阶依赖估计器的更优组合方式.在36 个加州大学机器学习数据集上的实验表明,基于散度的AODE属性加权算法显著优于原始的AODE算法.因此,散度加权能够有效提升AODE的分类性能.
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引用本文复制引用
基金项目
国家自然科学基金(62276136)
江苏省研究生科研与实践创新计划项目(SJCX23_1105)
出版年
2024
国家科技期刊平台
NSTL
万方数据