Replace Missing Values with EM algorithm based on GMM and Naïve Bayesian

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.