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.
 “The Standish Group Report: Chaos 2001,” 2001, http://standishgroup.com/sample research/PDFpages/ ex-treme chaos.pdf.
 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.
 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.
 G. Boetticher, T. Menzies, and T. Ostrand, “The PROM-ISE Repository of Empirical Software Engineering Data,” 2007, http://promisedata.org/repository.
 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.
 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:// citeseer.ist.psu.edu/domingos97 optimality. html
 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, http://www.csse.monash.edu/_webb/Files/YangWe-bb03.pdf.
 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, http://citeseer.ist.psu.edu/john95 estimating.html.
 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, http://www.cs.waikato.ac.nz/_mhall/HallHolmesTKDE.pdf.
 J. Dougherty, R. Kohavi, and M. Sahami, “Supervised and unsupervised discretization of continuous features,” in International Conference on Machine Learning, pp. 194–202, 1995, http://www.cs.pdx.edu/_timm/dm/dougherty95supervised.pdf.
 T. Menzies, J. Greenwald, and A. Frank, “Data mining static code attributes to learn defect predictors,” IEEE Transactions on Software Engineering, January 2007, http://menzies.us/pdf/06learnPredict.pdf.
 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.
 A. Beygelzimer, S. Kakade, and J. Langford, “Cover trees for nearest neighbor,” in ICML’06, 2006, http://hunch.net/_jl/projects/cover tree/cover tree.html.
 S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimi-zation by simulated annealing,” Science, No. 4598, Vol. 220, pp. 671–680, 1983, http://citeseer.nj.nec.com/kirkpatrick83opt-imization.html
 G. G. Towell and J. W. Shavlik, “Extracting refined rules from knowledge-based neural networks,” Machine Learning, Vol. 13, pp. 71–101, 1993, http: //citeseer.ist.psu.edu/towell92extracting.html
 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.
 T. Menzies and E. Sinsel, “Practical large scale what-if queries: Case studies with software risk assessment,” in Proceedings ASE 2000, 2000, http://menzies.us/pdf/00ase.pdf.
 W. Cohen, “Fast effective rule induction,” in ICML’95, 1995, pp. 115–123, http://www.cs.cmu.edu/_wcohen/postscript/ml-95-ripper.ps.
 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, http://menzies.us/pdf/03blind.pdf.
 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, http://www.csse.monash.edu/_webb/Files/LuYangWebb06.pdf.
 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.
 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. http://www.liacc.up.pt/_jgama/ IWKDDS/Papers/p6.pdf.
 R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, Vol. 97, No. 1-2, pp. 273–324, 1997, http://citeseer.nj.nec.com/ kohavi96wrappers.html
 T. Menzies and J. D. Stefano, “More success and failure factors in software reuse,” IEEE Transactions on Soft-ware Engineering, May 2003, http://men- zies.us/pdf/02sereuse.pdf.
 T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting best practices for effort estimation,” IEEE Transactions on Software Engineering, November 2006, http://menzies.us/pdf/06coseekmo.pdf.
 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.
 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, http://citeseer.nj.nec.com/ provost98case.html.
 R. Bouckaert, “Choosing between two learning algo-rithms based on calibrated tests,” in ICML’03, 2003, http://www.cs.pdx.edu/_timm/dm/10x 10way.
 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.