Empirical evaluation of cost overrun prediction with imbalance data

Masateru Tsunoda, Akito Monden, Jun Ichiro Shibata, Ken Ichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

To prevent cost overrun of software projects, it is necessary for project managers to identify projects which have high risk of cost overrun in the early phase. So far, discriminant methods such as linear discriminant analysis and logistic regression have been used to predict cost overrun projects. However, accuracy of discriminant methods often becomes low when a dataset used for predict is imbalanced, i.e. there exists a large difference between the number of cost overrun projects and non cost overrun projects. In this paper, we compared accuracy of linear discriminant analysis, logistic regression, classification tree, Mahalanobis-Taguchi method, and collaborative filtering, by changing the percentage of cost overrun projects in the dataset. The result showed that collaborative filtering was highest accuracy among five methods. When the number of cost overrun projects and non cost overrun was balanced in the dataset, linear discriminant analysis was second highest accuracy, and when it was not balanced, Mahalanobis-Taguchi method was second highest among five methods.

Original languageEnglish
Title of host publication2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011
Pages415-420
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011 - Sanya, Hainan Island, China
Duration: May 16 2011May 18 2011

Publication series

NameProceedings - 2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011

Other

Other2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011
CountryChina
CitySanya, Hainan Island
Period5/16/115/18/11

Keywords

  • Collaborative Filtering
  • Mahalanobis-Taguchi method
  • biased data
  • failure prone project
  • risk management

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems

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  • Cite this

    Tsunoda, M., Monden, A., Shibata, J. I., & Matsumoto, K. I. (2011). Empirical evaluation of cost overrun prediction with imbalance data. In 2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011 (pp. 415-420). [6086505] (Proceedings - 2011 10th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2011). https://doi.org/10.1109/ICIS.2011.71