摘要
为了提升K阶依赖贝叶斯分类(KDB)模型的条件依赖表达能力,本文以Markov blanket的特征提取思想为基本原则,降低特征属性间的条件独立性,根据贪婪搜索策略进行贝叶斯分类模型的结构学习。基于训练样本集构建宏观模型,基于测试样本构建微观模型,最终通过集成模型进行决策。针对UCI机器学习数据集进行交叉验证,实验结果分别从0-1损失、偏差和方差等角度证明了本文算法的合理性和有效性。
In order to promote the conditional dependence expression ability of K-dependence Bayesian Classifier,in this paper we relax the conditional independence between predictive features according to the basic idea of Markov blanket for feature extraction,and carry out structure learning based on greedy search strategy.We propose a macro model from the training set,and a micro model from each test sample.The decision is made by the ensemble of both models.We select data sets from UCI machine learning repository for cross validation.The experimental results demonstrate the rationality and effectiveness of the proposed algorithm in terms of 0-1 loss,bias and variance.
作者
王利民
刘洋
孙铭会
李美慧
WANG Li-min;LIU Yang;SUN Ming-hui;LI Mei-hui(College of Computer Science and Technology,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2018年第6期1851-1858,共8页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61272209)
吉林省自然科学基金项目(20150101014JC)
