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A Missing Data Imputation by Combining K Nearest Neighbor with Maximum Likelihood Estimation for Numerical Software Project Data  

Lee, Dong-Ho (KAIST 전산학과)
Yoon, Kyung-A (KAIST 전산학과)
Bae, Doo-Hwan (KAIST 전산학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.4, 2009 , pp. 273-282 More about this Journal
Abstract
Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.
Keywords
missing data imputation; K-NN; maximum likelihood estimation; software project data;
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