Hybrid Missing Data Technique using Imputation and Deletion of Missing Data

Abstract

In this paper, we propose a hybrid missing data technique combine deletion and imputation of missing data. First, the proposed method deletes projects and/or metrics with high missing rate. Next, the proposed method imputes using analogy based imputation for remaining missing data. In the experiment, we compared estimation accuracies of multivariate liner regression in software development dataset. The results showed that mean balanced relative error was improved than conventional method.