操作风险等级预测的朴素贝叶斯方法研究

Naive Bayes method in operational risk level prediction

操作风险数据积累比较困难,而且往往不完整,朴素贝叶斯分类器是目前进行小样本分类最优秀的分类器之一,适合于操作风险等级预测。在对具有完整数据朴素贝叶斯分类器学习和分类的基础上,提出了基于星形结构和Gibbs sampling的具有丢失数据朴素贝叶斯分类器学习方法,能够避免目前常用的处理丢失数据方法所带来的局部最优、信息丢失和冗余等方面的问题。

It is difficult to accumulate a large number of data with high quality in operational risk.Naive Bayes classifier is the one of best classifiers used to small data set classification.It is suitable for operational risk level prediction.In this paper,firstly,the process of learning and classing is presented on naive Bayes classifier with complete data sets.Then,a method naive Bayes classifier learning with missing data is developed based on star structure and Gibbs sampling.The existing problems can be avoided in local optimization,information losing and redundancy.