关键词: 丢失数据填充, 参数更新器, 最大期望值算法（EM）, 贝叶斯网络

Abstract: Dataset with missing values is quite common in real applications，and handling missing values has become a research hot issue in the classification field.This paper analyzes and compares several popular missing values imputation algorithms，and has proposed a novel imputation algorithm for missing values based on EM（Expectation Maximization） and Bayesian network.In this algorithm，the Na?觙ve Bayesian is employed to estimate the initial values of EM algorithm，and the EM inspired approach for filling up missing values is incorporated to Bayesian network learning with the objective of ensuring the ultimate updater.As a result，the complete dataset is got after imputation.Experiment results demonstrate that the proposed algorithm enables much higher classification accuracy and lower cost when compared with other classical imputation algorithm.

Key words: missing values imputation, parameter updater, Expectation-Maximization（EM）, Bayesian network