Evaluation of Missing Value Imputation Methods for Effort Estimation Using Liner Regression

• TODA Koji

  Faculty of Information Engineering, Fukuoka Institute of Technology

• TSUNODA Masateru

  Faculty of Science and Engineering, Kinki University

Bibliographic Information

Other Title

• 重回帰分析を用いた工数予測における欠損値補完手法の性能比較

Abstract

Multivariate regression models have been commonly used to estimate the software development effort to assist project planning and/or management. Since project data sets for model construction often contain missing values, we need to build a complete data set that has no missing values either by using imputation methods. However, while there are several ways to build the complete data set, it is unclear which method is the most suitable for the project data set. In this paper, using project data of 1364 cases (34% missing value rate) collected from several companies, we applied four imputation methods (k-nn method, applied CF method, Miss Forest method and Multiple Imputation method) to build regression models. Then, using project data of 160 cases (having no missing values), we evaluated the estimation performance of models after applying each imputation method. The result showed that Multiple Imputation method showed the best performance.

Journal

• Computer Software

  Computer Software 34 (4), 4_150-4_155, 2017

  Japan Society for Software Science and Technology

Details

• CRID: 1390001204736869120
• NII Article ID: 130006855224
• DOI: 10.11309/jssst.34.4_150
• ISSN: 02896540
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed