期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2014
卷号：7
期号：2
页码：347-356
DOI：10.14257/ijhit.2014.7.2.30
出版社：SERSC
摘要：This paper proposed a new weighted KNN data filling algorithm based on grey correlation analysis (GBWKNN) by researching the nearest neighbor of missing data filling method. It is aimed at that missing data is not sensitive to noise data and combined with grey system theory and the advantage of the K nearest neighbor algorithm. The experimental results on six UCI data sets showed that its filling accuracy is better than the traditional method of K nearest neighbor and filling algorithm presented by Huang and Lee.
关键词：missing data; grey correlation analysis; data filling
