期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：5
页码：177-188
DOI：10.14257/ijseia.2014.8.5.14
出版社：SERSC
摘要：In data mining applications, there are various kinds of missing values in experimental datasets. Non-substitution or inappropriate treatment of missing values has a high probability to cause a lot of warnings or errors. Besides, many classification algorithms are very sensitive to the missing values. Because of these, handling the missing values is an important phase in many classification or data mining task. This paper introduces traditional EM algorithm and disadvantage of the EM algorithm. We propose a new method to implement the missing values based on EM algorithm, which uses Naive Bayesian to improve the accuracy. We conclude by classifying seeds dataset and vertebral columns dataset and comparing the results to those obtained by applying two other missing value handling methods: the traditional EM algorithm and the non-substitution method. The experimental results prove a stable algorithm for improving the data classification accuracy on large datasets, which contain a lot of missing values.
关键词：missing values; EM algorithm; GMM; Naive Bayesian
