Missing data imputation based on unsupervised simple competitive learning

Authors:
Byoung Jik Lee
Affiliations:
Department of Computer Science, Western Illinois University, Macomb, IL
Venue:
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2010

Citing 3
Cited 0

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Abstract

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.