摘要
针对不确定性数据的分类问题,提出一种基于直方图估计的不确定性朴素贝叶斯分类器(HU-NBC).基于直方图估计的思想,建立估计不确定性数据概率密度函数的数学模型,并利用该模型估计不确定性朴素贝叶斯分类器的类条件概率密度函数.实验结果表明,与同类型算法相比,基于直方图估计的HU-NBC算法拥有较优的分类精度、较小的时间代价和空间需求,适合解决数据量较大的不确定性数据分类问题.
Aiming at the classification problem with uncertain data, a naive Bayes classification method is proposed in this paper-uncertainty naive Bayes classifier based on histogram estimation （HU-NBC）. Based on the idea of histogram estimation, an estimation model of novel probability density functions is established for uncertain data and is used to estimate the class-conditional probability density functions of uncertainty naive Bayes classifier. Experimental results on UCI datasets show that HU-NBC has a good classifying accuracy, less runtime and memory requirements compared with existing methods, and it is suitable for classification with large amounts of data.
出处
《江西理工大学学报》
CAS
2014年第5期96-100,共5页
Journal of Jiangxi University of Science and Technology
基金
国家自然科学基金资助项目(41362015)
江西省自然科学基金资助项目(20122BAB201045)
关键词
不确定性数据
朴素贝叶斯
直方图估计
类条件概率密度
分类
uncertain data
naive Bayes
histogram estimation
class- conditional probability density
classification
