Share This Article:

Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects

Abstract Full-Text HTML Download Download as PDF (Size:466KB) PP. 221-236
DOI: 10.4236/jsea.2009.24030    5,357 Downloads   9,618 Views   Citations


Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

T. MENZIES, O. MIZUNO, Y. TAKAGI and T. KIKUNO, "Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects," Journal of Software Engineering and Applications, Vol. 2 No. 4, 2009, pp. 221-236. doi: 10.4236/jsea.2009.24030.


[1] Y. Takagi, O. Mizuno, and T. Kikuno, “An empirical approach to characterizing risky software projects based on logistic regression analysis,” Empirical Software En-gineering, Vol. 10, No. 4, pp. 495–515, 2005.
[2] S. Abe, O. Mizuno, T. Kikuno, N. Kikuchi, and M. Hira-yama, “Estimation of project success using bayesian clas-sifier,” in ICSE 2006, pp. 600–603, 2006.
[3] O. Mizuno, T. Kikuno, Y. Takagi, and K. Sakamoto, “Characterization of risky projects based on project man-agers evaluation,” in ICSE 2000, 2000.
[4] R. Glass, “Software runaways: Lessons learned from massive software project failures,” Pearson Education, 1997.
[5] “The Standish Group Report: Chaos 2001,” 2001, research/PDFpages/ ex-treme chaos.pdf.
[6] J. Jiang, G. Klein, H. Chen, and L. Lin, “Reducing user-related risks during and prior to system develop-ment,” International Journal of Project Management, Vol. 20, No. 7, pp. 507–515, October 2002.
[7] J. Ropponen and K. Lyytinen, “Components of software development risk: how to address them? A project man-ager survey,” IEEE Transactions on Software Engineer-ing, pp. 98–112, Feburary 2000.
[8] W. Dillon and M. Goldstein, “Multivariate analysis: Methods and applications.” Wiley-Interscience, 1984.
[9] J. C. Munson and T. M. Khoshgoftaar, “The use of soft-ware complexity metrics in software reliability model-ing,” in Proceedings of the International Symposium on Software Reliability Engineering, Austin, TX, May 1991.
[10] G. Boetticher, T. Menzies, and T. Ostrand, “The PROM-ISE Repository of Empirical Software Engineering Data,” 2007,
[11] T. McCabe, “A complexity measure,” IEEE Transactions on Software Engineering, Vol. 2, No. 4, pp. 308–320, December 1976.
[12] M. Halstead, “Elements of software science,” Elsevier, 1977.
[13] K. Toh, W. Yau, and X. Jiang, “A reduced multivariate polynomial model for multimodal biometrics and classi-fiers fusion,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 224–233, February 2004.
[14] R. Duda, P. Hart, and N. Nilsson, “Subjective bayesian methods for rule-based inference systems,” in Technical Report 124, Artificial Intelligence Center, SRI Interna-tional, 1976.
[15] P. Domingos and M. J. Pazzani, “On the optimality of the simple bayesian classifier under zero-one loss,” Machine Learning, Vol. 29, No. 2-3, pp. 103–130, 1997. http:// optimality. html
[16] Y. Yang and G. Webb, “Weighted proportional k-interval discretization for naive-bayes classifiers,” in Proceedings of the 7th Pacific-Asia Conference on Knowledge Dis-covery and Data Mining (PAKDD 2003), 2003,
[17] I. H. Witten and E. Frank, Data mining. 2nd edition. Los Altos, Morgan Kaufmann, US, 2005.
[18] G. John and P. Langley, “Estimating continuous distribu-tions in bayesian classifiers,” in Proceedings of the Elev-enth Conference on Uncertainty in Artificial Intelligence Montreal, Quebec: Morgan Kaufmann, 1995, pp. 338–345, estimating.html.
[19] M. Hall and G. Holmes, “Benchmarking attribute selec-tion techniques for discrete class data mining,” IEEE Transactions On Knowledge And Data Engineering, Vol. 15, No. 6, pp. 1437–1447, 2003,
[20] J. Dougherty, R. Kohavi, and M. Sahami, “Supervised and unsupervised discretization of continuous features,” in International Conference on Machine Learning, pp. 194–202, 1995,
[21] T. Menzies, J. Greenwald, and A. Frank, “Data mining static code attributes to learn defect predictors,” IEEE Transactions on Software Engineering, January 2007,
[22] R. Quinlan, C4.5: Programs for Machine Learning. Mor-gan Kaufman, 1992.
[23] R. Holte, “Very simple classification rules perform well on most commonly used datasets,” Machine Learning, Vol. 11, pp. 63, 1993.
[24] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, “Classification and regression trees,” Wadsworth Interna-tional, Monterey, CA, Tech. Rep., 1984.
[25] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967.
[26] T. M. Cover and P. E. Hart, “Nearest neighbour pattern classification,” IEEE Transactions on Information Theory, pp. 21–27, January 1967.
[27] A. Beygelzimer, S. Kakade, and J. Langford, “Cover trees for nearest neighbor,” in ICML’06, 2006, tree/cover tree.html.
[28] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimi-zation by simulated annealing,” Science, No. 4598, Vol. 220, pp. 671–680, 1983,
[29] G. G. Towell and J. W. Shavlik, “Extracting refined rules from knowledge-based neural networks,” Machine Learning, Vol. 13, pp. 71–101, 1993, http: //
[30] B. Taylor and M. Darrah, “Rule extraction as a formal method for the verification and validation of neural net-works,” in IJCNN ’05: Proceedings. 2005 IEEE Interna-tional Joint Conference on Neural Networks, Vol. 5, pp. 2915–2920, 2005.
[31] T. Menzies and E. Sinsel, “Practical large scale what-if queries: Case studies with software risk assessment,” in Proceedings ASE 2000, 2000,
[32] W. Cohen, “Fast effective rule induction,” in ICML’95, 1995, pp. 115–123,
[33] J. Cendrowska, “Prism: An algorithm for inducing modular rules,” International Journal of Man-Machine Studies, Vol. 27, No. 4, pp. 349–370, 1987.
[34] T. Dietterich, “Machine learning research: Four current directions,” AI Magazine, Vol. 18, No. 4, pp. 97–136, 1997.
[35] T. Menzies and J. S. D. Stefano, “How good is your blind spot sampling policy?” in 2004 IEEE Conference on High Assurance Software Engineering, 2003,
[36] J. Lu, Y. Yang, and G. Webb, “Incremental discretization for naive-bayes classifier,” in Lecture Notes in Computer Science 4093: Proceedings of the Second International Conference on Advanced Data Mining and Applications (ADMA 2006), pp. 223–238, 2006,
[37] U. M. Fayyad and I. H. Irani, “Multi-interval discretiza-tion of continuous-valued attributes for classification learning,” in Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027, 1993.
[38] J. Gama and C. Pinto, “Discretization from data streams: Applications to histograms and data mining,” in SAC ’06: Proceedings of the 2006 ACM symposium on Applied computing. New York, NY, USA: ACM Press, pp. 662–667, 2006. IWKDDS/Papers/p6.pdf.
[39] A. Miller, Subset Selection in Regression (second edition). Chapman & Hall, 2002.
[40] R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, Vol. 97, No. 1-2, pp. 273–324, 1997, kohavi96wrappers.html
[41] T. Menzies and J. D. Stefano, “More success and failure factors in software reuse,” IEEE Transactions on Soft-ware Engineering, May 2003, http://men-
[42] T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting best practices for effort estimation,” IEEE Transactions on Software Engineering, November 2006,
[43] U. Fayyad, “Data mining and knowledge discovery in databases: Implications for scientific databases,” in Pro-ceedings on Ninth International Conference on Scientific and Statistical Database Management, pp. 2–11, 1997.
[44] F. Provost, T. Fawcett, and R. Kohavi, “The case against accuracy estimation for comparing induction algorithms,” in Proc. 15th International Conf. on Ma-chine Learning. Morgan Kaufmann, San Francisco, CA, pp. 445–453, 1998, provost98case.html.
[45] R. Bouckaert, “Choosing between two learning algo-rithms based on calibrated tests,” in ICML’03, 2003, 10way.
[46] C. Kirsopp and M. Shepperd, “Case and feature subset selection in case-based software project effort predic-tion,” in Proc. of 22nd SGAI International Conference on Knowledge-Based Systems and Applied Artificial Intel-ligence, Cambridge, UK, 2002.
[47] N. Nagappan and T. Ball, “Static analysis tools as early indicators of pre-release defect density,” in ICSE 2005, St. Louis, 2005.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.