Comparison of multilabel classification models to forecast project dispute resolutions

Expert Systems with Applications

Volumn 39, Issue 11, 2012, Pages 10202-10211

  Chou, Jui Sheng ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Data mining; Dispute resolutions; Forecasting; Multilabel classification; Procurement management; Public private partnership

Indexed keywords

ADMINISTRATIVE APPEALS; ANALYTICAL RESULTS; AUTOMATIC INTERACTION DETECTION; CART MODELS; CHI-SQUARED; CLASSIFICATION ACCURACY; CLASSIFICATION AND REGRESSION TREE; CLASSIFICATION MODELS; COMBINED TECHNIQUES; DISPUTE RESOLUTION; ENSEMBLE MODELS; MACHINE LEARNERS; MODEL ANALYSIS; MULTI-LABEL; PROCUREMENT MANAGEMENT; PROJECT EXECUTION; PUBLIC INFRASTRUCTURE PROJECT; PUBLIC PRIVATE PARTNERSHIPS;

DATA MINING; DECISION SUPPORT SYSTEMS; FORESTRY; INTERNET PROTOCOLS; LAWS AND LEGISLATION; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

FORECASTING;

CLASSIFICATION; DATA PROCESSING; DECISION THEORY; FORECASTS; INTERNET; PARTNERSHIPS; PLANNING;

EID: 84859434275     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.02.103     Document Type: Article

References (49)

1. M.P. Abednego, and S.O. Ogunlana Good project governance for proper risk allocation in Public-private partnerships in Indonesia International Journal of Project Management 24 2006 622 634
2. P.J.L. Adeodato, A.L. Arnaud, G.C. Vasconcelos, R.C.L.V. Cunha, and D.S.M.P. Monteiro MLP ensembles improve long term prediction accuracy over single networks International Journal of Forecasting 27 2011 661 671
3. L.A. Alexandre, A.C. Campilho, and M. Kamel On combining classifiers using sum and product rules Pattern Recognition Letters 22 2001 1283 1289
4. R.R. Andrawis, A.F. Atiya, and H. El-Shishiny Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition International Journal of Forecasting 27 2011 672 688
5. D. Arditi, F.E. Oksay, and O.B. Tokdemir Predicting the outcome of construction litigation using neural networks Computer-Aided Civil and Infrastructure Engineering 13 1998 75 81
6. D. Arditi, and T. Pulket Predicting the outcome of construction litigation using boosted decision trees Journal of Computing in Civil Engineering 19 2005 387 393
7. D. Arditi, and T. Pulket Predicting the outcome of construction litigation using an integrated artificial intelligence model Journal of Computing in Civil Engineering 24 2010 73 80
8. D. Arditi, and O.B. Tokdemir Comparison of case-based reasoning and Artificial Neural Networks Journal of Computing in Civil Engineering 13 1999 162 169
9. D. Arditi, and O.B. Tokdemir Using case-based reasoning to predict the outcome of construction litigation Computer-Aided Civil and Infrastructure Engineering 14 1999 385 393
10. D. Biggs, B.d. Ville, and E. Suen A method of choosing mutiway partitions for classification and decision trees Journal of Applied Statistics 18 1991 49 62
11. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Chapman & Hall, CRC New York
12. K.W. Chau Application of a PSO-based neural network in analysis of outcomes of construction claims Automation in Construction 16 2007 642 646
13. K.-Y. Chen Forecasting systems reliability based on support vector regression with genetic algorithms Reliability Engineering & System Safety 92 2007 423 432
14. J.-H. Chen KNN based knowledge-sharing model for severe change order disputes in construction Automation in Construction 17 2008 773 779
15. J.-H. Chen, and S.C. Hsu Hybrid ANN-CBR model for disputed change orders in construction projects Automation in Construction 17 2007 56 64
16. M.-Y. Cheng, H.-C. Tsai, and Y.-H. Chiu Fuzzy case-based reasoning for coping with construction disputes Expert Systems with Applications 36 2009 4106 4113
17. S.-O. Cheung Critical factors affecting the use of alternative dispute resolution processes in construction International Journal of Project Management 17 1999 189 194
18. S.-O. Cheung, H.C.H. Suen, and T.-I. Lam Fundamentals of alternative dispute resolution processes in construction Journal of Construction Engineering and Management 128 2002 409 417
19. J.-S. Chou, C.-K. Chiu, M. Farfoura, and I. Al-Taharwa Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques Journal of Computing in Civil Engineering 25 2011 242 253
20. J.-S. Chou, Y. Tai, and L.-J. Chang Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models International Journal of Production Economics 128 2010 339 350
21. C. Clifton, and C.F. Duffield Improved PFI/PPP service outcomes through the integration of Alliance principles International Journal of Project Management 24 2006 573 586
22. I.H. El-Adaway, and A.A. Kandil Multiagent system for construction dispute resolution (MAS-COR) Journal of Construction Engineering and Management 136 2010 303 315
23. N. Friedman, D. Geiger, M. Goldszmidt, G. Provan, P. Langley, and P. Smyth Bayesian network classifiers Machine Learning 29 1997 131 163
24. R.J. Gebken, and G. Edward Gibson Quantification of costs for dispute resolution procedures in the construction industry Journal of Professional Issues in Engineering Education and Practice 132 2006 264 271
25. S. Haykin Neural networks: A comprehensive foundation 2nd ed. 1999 Prentice-Hall New York
26. IBM, (2010). PASW modeler. NY, USA: IBM Corporation.
27. D. Jones Construction project dispute resolution: Options for effective dispute avoidance and management Journal of Professional Issues in Engineering Education and Practice 132 2006 225 235
28. M. Kassab, T. Hegazy, and K. Hipel Computerized DSS for construction conflict resolution under uncertainty Journal of Construction Engineering and Management 136 2010 1249 1257
29. F.S. Keith Alternative dispute resolution in government Journal of Management in Engineering 13 1997 25 28
30. H.-J. Kim, and K.-S. Shin A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets Applied Soft Computing 7 2007 569 576
31. M.-C. Lee Using support vector machine with a hybrid feature selection method to the stock trend prediction Expert Systems with Applications 36 2009 10896 10904
32. L. Li, W. Jiang, X. Li, K.L. Moser, Z. Guo, and L. Du A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset Genomics 85 2005 16 23
33. W.-Y. Loh, and Y.-S. Shih Split selection methods for classification trees Statistica Sinica 7 1997 815 840
34. S.-H. Min, J. Lee, and I. Han Hybrid genetic algorithms and Support Vector Machines for bankruptcy prediction Expert Systems with Applications 31 2006 652 660
35. S. Nandi, Y. Badhe, J. Lonari, U. Sridevi, B.S. Rao, and S.S. Tambe Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: Study of benzene isopropylation on Hbeta catalyst Chemical Engineering Journal 97 2004 115 129
36. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models (4th ed.). IRWIN.
37. PCC, (2010). The Strategies to the promotion of public-private partnerships (in Chinese). In Taipei, Taiwan (ROC): Public Construction Commission, Executive Yuan.
38. PCC, (2011). Engineering evaluation forum of PPP strategy (in Chinese). In (Vol. 2011). Taipei, Taiwan (ROC): Public Construction Commission, Executive Yuan.
39. T. Pulket, and D. Arditi Universal prediction model for construction litigation Journal of Computing in Civil Engineering 23 2009 178 187
40. Quinlan, J. R. (2007). Data Mining Tools See5 and C5.0. < http://www.rulequest.com/see5-info.html >.
41. G. Shmueli, N.R. Patel, and P.C. Bruce Data mining for business intelligence 2007 John Wiley and Sons, Inc New Jersey
42. SPSS, (2007). Clementine 12.0 algorithms guide. Chicago, USA: Integral Solutions Limited.
43. P.-N. Tan, M. Steinbach, and V. Kumar Introduction to data mining 2006 Pearson Education, Inc
44. V.N. Vapnik Statistical learning theory 1998 John Wiley and Sons New York
45. The World Bank Group (WBG). (2011). .
46. J.D. Wichard Forecasting the NN5 time series with hybrid models International Journal of Forecasting 27 2011 700 707
47. I.H. Witten, and E. Frank Data mining: Practical machine learning tools and techniques 2nd ed. 2005 Kaufmann
48. Q. Wu The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine Expert Systems with Applications 37 2010 1776 1783
49. C.-H. Wu, G.-H. Tzeng, and R.-H. Lin A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression Expert Systems with Applications 36 2009 4725 4735