Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
  1.1. Traditional EM Algorithm
  1.2. EM algorithm with Naive Bayesian
  1.3. Code Implementation
 2. Data Implement and Classification Result
  2.1. Data Implementation
  2.2. Implement the Missing Values
  2.3. Classification Results
 3. Experimental Results
 Acknowledgment
 References

키워드

저자

• Xi-Yu Zhou [ I.T. College Gachon University Seongnam, South Korea ]
• Joon S. Lim [ I.T. College Gachon University Seongnam, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605