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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 601960, 11 pages
http://dx.doi.org/10.1155/2014/601960
Research Article

An Endogenous Project Performance Evaluation Approach Based on Random Forests and IN-PROMETHEE II Methods

1School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China
2School of Civil Engineering, Tsinghua University, Beijing 100084, China
3China Economics and Management Academy, Central University of Finance and Economics, Beijing 100081, China
4School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China

Received 25 May 2014; Accepted 29 July 2014; Published 28 October 2014

Academic Editor: Tofigh Allahviranloo

Copyright © 2014 Na Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H.-Y. Wu, G.-H. Tzeng, and Y.-H. Chen, “A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard,” Expert Systems with Applications, vol. 36, no. 6, pp. 10135–10147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. D. West, “Neural network credit scoring models,” Computers and Operations Research, vol. 27, no. 11-12, pp. 1131–1152, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. D. D. Wu, “Performance evaluation: an integrated method using data envelopment analysis and fuzzy preference relations,” European Journal of Operational Research, vol. 194, no. 1, pp. 227–235, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  4. J. Ma, Z.-P. Fan, and L.-H. Huang, “A subjective and objective integrated approach to determine attribute weights,” European Journal of Operational Research, vol. 112, no. 2, pp. 397–404, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Joshi, D. K. Banwet, and R. Shankar, “A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain,” Expert Systems with Applications, vol. 38, no. 8, pp. 10170–10182, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. T. L. Saaty, “A scaling method for priorities in hierarchical structures,” Journal o fMathematical Psychology, vol. 15, no. 3, pp. 234–281, 1977. View at Google Scholar · View at MathSciNet · View at Scopus
  7. F. Torfi, R. Z. Farahani, and S. Rezapour, “Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 520–528, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. T. Chu, R. E. Kalaba, and K. Spingarn, “A comparison of two methods for determining the weights of belonging to fuzzy sets,” Journal of Optimization Theory and Applications, vol. 27, no. 4, pp. 531–538, 1979. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  9. E. Giovannini, M. Nardo, M. Saisana, A. Saltelli, S. Tarantola, and A. Hoffman, Handbook on Constructing Composite Indicators: Methodology and User Guide, Organisation for Economic Cooperation and Development, 3rd edition, 2005.
  10. T. Sueyoshi and M. Goto, “Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment,” European Journal of Operational Research, vol. 199, no. 2, pp. 561–575, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  11. D.-F. Li, G.-H. Chen, and Z.-G. Huang, “Linear programming method for multiattribute group decision making using IF sets,” Information Sciences, vol. 180, no. 9, pp. 1591–1609, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  13. A. M. Prasad, L. R. Iverson, and A. Liaw, “Newer classification and regression tree techniques: bagging and random forests for ecological prediction,” Ecosystems, vol. 9, no. 2, pp. 181–199, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. D. S. Palmer, N. M. O'Boyle, R. C. Glen, and J. B. O. Mitchell, “Random forest models to predict aqueous solubility,” Journal of Chemical Information and Modeling, vol. 47, no. 1, pp. 150–158, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, “Random forests for land cover classification,” Pattern Recognition Letters, vol. 27, no. 4, pp. 294–300, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Fanelli, J. Gall, and L. van Gool, “Real time head pose estimation with random regression forests,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 617–624, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. R. Sikonja, Improving Random Forests, Springer, Berlin, Germany, 2004.
  18. J. P. Brans and B. Mareschal, PROMETHEE Methods, Springer, New York, NY, USA, 2005.
  19. O. Senvar, G. Tuzkaya, and C. Kahraman, Multi Criteria Supplier Selection Using Fuzzy PROMETHEE Method, Springer, Berlin, Germany, 2014.
  20. A. O. Kaya, T. Kaya, and C. Kahraman, “A fuzzy approach to urban ecotourism site selection based on an integrated promethee III methodology,” Multiple-Valued Logic and Soft Computing, vol. 21, no. 1, pp. 89–111, 2013. View at Google Scholar
  21. Z. Taha and S. Rostam, “A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell,” Journal of Intelligent Manufacturing, vol. 23, no. 6, pp. 2137–2149, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. C.-b. Chen and C. M. Klein, “An efficient approach to solving fuzzy MADM problems,” Fuzzy Sets and Systems, vol. 88, no. 1, pp. 51–67, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. L. Tran and L. Duckstein, “Comparison of fuzzy numbers using a fuzzy distance measure,” Fuzzy Sets and Systems, vol. 130, no. 3, pp. 331–341, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. M. Behzadian, R. B. Kazemzadeh, A. Albadvi, and M. Aghdasi, “PROMETHEE: a comprehensive literature review on methodologies and applications,” European Journal of Operational Research, vol. 200, no. 1, pp. 198–215, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. J. P. Vincke and P. H. Brans, “A preference ranking organization method. The PROMETHEE method for MCDM,” Management Science, vol. 31, no. 6, pp. 641–656, 1985. View at Google Scholar
  26. Y. M. Wang and J. K. Zhang, “A method based on standard and mean deviations for determining the weight coefficients of multiple attributes and its applications,” Mathematical Statistics and Management, vol. 22, no. 1, pp. 22–26, 2003. View at Google Scholar