摘要
C3 I系统在解决威胁度估计等问题时 ,应根据环境恰当确定影响威胁度等指标的诸属性 .提出了基于粗集的贝叶斯分类器算法 ,该算法在基于粗集的属性约简方法的基础上 ,综合考虑条件属性和决策属性间的依赖性以及条件属性间的依赖性对约简的影响 .通过基于依赖性的属性约简 ,改善属性变量间独立性限制 ,发挥贝叶斯分类器的鲁棒性潜能 ,优化贝叶斯分类器性能 .实验证明用该方法解决威胁度估计等 C3 I系统中的问题 ,效果良好 .
In C 3I, for efficiently resolving some problems, such as threat-degree estimation, etc., the features that affect these objects should be correctly determined according to battlefield conditions. So a rough set-based method for constructing simple Bayesian classifier from databases was presented here. On the basis of the feature reduction algorithm based on rough set, this method takes synthetically into account the influence of the dependency of condition features and decision-making feature towards reduction, and the influence of the dependency among condition features towards reduction. By the dependency-based feature reduction, this method improves the independency limit among feature variables, so that the robust potential of simple Bayesian classifier is utilized and the performance of simple Bayesian classifier from databases is optimized. When those problems, such as threat-degree estimation, etc., were dealt with by this method in C 3I, obtained well experimental results.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2003年第1期83-86,共4页
Transactions of Beijing Institute of Technology
基金
总装备部"十五"预研项目 ( 10 40 5 0 33)
