Missing Data Imputation based on Unsupervised Simple Competitive Learning

被引：0
|
作者：
Lee, Byoung Jik [1 ]
机构：
[1] Western Illinois Univ, Dept Comp Sci, Macomb, IL 61455 USA
来源：
PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES | 2010年
关键词：
Missing data; Datamining; Imputation; Simple competitive learning; Clustering; Machine learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an unsupervised learning method of data imputation technique for missing values. The proposed imputation method avoids getting stuck to the major groups in the dataset. The advantage of the system is demonstrated with different natures of data set; animal classification dataset and 1984 U.S. Congressional Voting Records dataset. The experimental result shows that the imputed dataset based on the proposed Simple Competitive Learning technique is efficient and robust.
引用
收藏
页码：292 / +
页数：2
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