Geeta Sikka and Arvinder Kaur Takkar and Moin Uddin
Comparison of Imputation Techniques for Efficient Prediction of Software Fault Proneness in Classes
271 - 274
2010
4
2
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/9138
https://publications.waset.org/vol/38
World Academy of Science, Engineering and Technology
Missing data is a persistent problem in almost all
areas of empirical research. The missing data must be treated very
carefully, as data plays a fundamental role in every analysis.
Improper treatment can distort the analysis or generate biased results.
In this paper, we compare and contrast various imputation techniques
on missing data sets and make an empirical evaluation of these
methods so as to construct quality software models. Our empirical
study is based on NASAs two public dataset. KC4 and KC1. The
actual data sets of 125 cases and 2107 cases respectively, without
any missing values were considered. The data set is used to create
Missing at Random (MAR) data Listwise Deletion(LD), Mean
Substitution(MS), Interpolation, Regression with an error term and
ExpectationMaximization (EM) approaches were used to compare
the effects of the various techniques.
Open Science Index 38, 2010