摘要
提出一种基于选择性的贝叶斯分类器集成方法.该方法为避免数据预处理时的属性约简对分类效果的直接影响,在训练集上通过随机属性选取生成若干属性子集,并以这些子集训练不同的贝叶斯分类器,进而采用遗传算法优选贝叶斯分类器集成,其适应度函数综合了分类器的精度和差异度两项指标.实验中,将该方法与已有方法在UCI的标准数据集上进行了性能比较,并将该方法用于C3I系统中的威胁度估计.
To avoid the influence of feature reduction from data pre-processing on the performance of classification, a selective approach was proposed for an ensemble of simple Bayesian classifiers(ESBC), making use of random feature selection to generate several feature subsets from the whole training set, and obtained different simple Bayesian classifiers(SBCs) with the feature subsets, and then optimized the ESBC using genetic algorithms (GA), wherein the fitness function of GA involved the accuracy and diversity of SBCs. In the experiments, this approach was compared with existing methods in their performance through some standard data sets from the UCI machine learning repository, and was applied to the threat-degree estimation in C3I.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2003年第6期724-727,共4页
Transactions of Beijing Institute of Technology
基金
国家部委预研项目(101405033)
关键词
贝叶斯分类器集成
随机属性选择
遗传算法
数据挖掘
ensemble of simple Bayesian classifiers
random feature selection
genetic algorithm
data mining
