Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation

  • Authors:

  • Qinbao Song;Martin Shepperd;Xiangru Chen;Jun Liu

  • Affiliations:

  • Department of Computer Science & Technology, Xi'an Jiaotong University, 28 Xian-Ning West Road, Xi'an, Shaanxi, 710049, China;School of IS, Computing & Maths, Brunel University, Uxbridge, UB8 3PH, United Kingdom;Department of Computer Science & Technology, Xi'an Jiaotong University, 28 Xian-Ning West Road, Xi'an, Shaanxi, 710049, China;Shaanxi Electric Power Training Center for the Staff Members, 21 Dian-Chang East Road, Xi'an, Shaanxi, 710038, China

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2008

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Abstract

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%.