Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables

Automation in Construction

Volumn 27, Issue , 2012, Pages 60-66

  Son, Hyojoo ^a   Kim, Changwan ^a   Kim, Changmin ^a  

^a Chung Ang University   (South Korea)

Author keywords

Cost performance prediction; Pre project planning; Principal component analysis; Project success; Support vector regression model

Indexed keywords

ACCURATE PREDICTION; COMMERCIAL BUILDING; COST PERFORMANCE; HYBRID PRINCIPAL COMPONENT ANALYSIS; PLANNING STAGES; PREDICTION OF COSTS; PREDICTIVE MODELS; PROJECT PERFORMANCE; PROJECT STAKEHOLDERS; PROJECT SUCCESS; SUPPORT VECTOR; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR REGRESSION MODELS;

FORECASTING; OFFICE BUILDINGS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

COSTS;

EID: 84863714416     PISSN: 09265805     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.autcon.2012.05.013     Document Type: Article

References (54)

1. CII Pre-project planning handbook Special Publication 39-2 1995 CII Austin, TX
2. G.E. Gibson Jr., Y.-R. Wang, C.-S. Cho, and M.P. Pappas What is preproject planning, anyway? Journal of Management in Engineering 22 1 2006 35 42
3. J.T. O'Connor, and C.G. Vickroy Control of construction project scope Source Document 6 1986 CII Austin, TX
4. P.R. Dumont, G.E. Gibson Jr., and J.R. Fish Scope management using project definition rating index Journal of Management in Engineering 13 5 1997 54 60
5. C.L. Menches, A.S. Hanna, E.V. Nordheim, and J.S. Russell Impact of pre-construction planning and project characteristics on performance in the US electrical construction industry Construction Management and Economics 26 8 2008 855 869
6. B.-G. Hwang, and J.W. Ho Front-end planning implementation in Singapore: status, importance and impact Journal of Construction Engineering Management 138 4 2012 567 573
7. Business Roundtable Confronting the Skilled Construction Work Force Shortage: A Blueprint for the Future 1997 Business Roundtable Washington, DC
8. G.E. Gibson Jr., and P.R. Dumont Project definition rating index (PDRI) Research Report 113-11 1996 CII Austin, TX
9. C.-S. Cho, and G.E. Gibson Jr. Building project scope definition using project definition rating index Journal of Architectural Engineering 7 4 2001 115 125
10. Y.-R. Wang, and G.E. Gibson Jr. A study of preproject planning and project success using ANNs and regression models Automation in Construction 19 3 2010 341 346
11. E.J. Jaselskis, Achieving construction project success through predictive discrete choice models, Ph.D. Thesis, University of Texas at Austin, Austin, TX, 1988.
12. K.R. Molenaar, and A.D. Songer Model for public sector design-build project selection Journal of Construction Engineering Management 124 6 1998 467 479
13. A.P.C. Chan, D.C.K. Ho, and C.M. Tam Design and build project success factors: multivariate analysis Journal of Construction Engineering Management 127 2 2001 93 100
14. F.Y.Y. Ling, S.L. Chan, E. Chong, and L.P. Ee Predicting performance of design-build and design-bid-build projects Journal of Construction Engineering Management 130 1 2004 75 83
15. S.H. Han, D.Y. Kim, and H. Kim Predicting profit performance for selecting candidate international construction projects Journal of Construction Engineering Management 133 6 2007 425 436
16. F.Y.Y. Ling, S.P. Low, S. Wang, and T. Egbelakin Models for predicting project performance in China using project management practices adopted by foreign AEC firms Journal of Construction Engineering Management 134 12 2008 983 990
17. D.K.H. Chua, P.K. Loh, Y.C. Kog, and E.J. Jaselskis Neural networks for construction project success Expert Systems with Applications 13 4 1997 317 328
18. F.Y.Y. Ling, and M. Liu Using neural network to predict performance of design-build projects in Singapore Building and Environment 39 10 2004 1263 1274
19. C.-H. Ko, and M.-Y. Cheng Dynamic prediction of project success using artificial intelligence Journal of Construction Engineering Management 133 4 2007 316 324
20. K. Hornik, M. Stinchcombe, and H. White Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks Neural Networks 3 5 1990 551 560
21. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag New York
22. C.L. Wu, K.W. Chau, and Y.S. Li River stage prediction based on a distributed support vector regression Journal of Hydrology 358 1-2 2008 96 111
23. B.V. Rao, and S.J. Gopalakrishna Hardgrove grindability index prediction using support vector regression International Journal of Mineral Processing 91 1-2 2009 55 59
24. C. Wang, C. Wu, and Y. Liang Medicine composition analysis based on PCA and SVM L. Wang, K. Chen, Y.S. Ong, Lecture Notes in Computer Science 2005 Springer-Verlag Berlin Heidelberg Heidelberg, Germany 1226 1230
25. X. Xu, and X. Wang An adaptive network intrusion detection method based on PCA and support vector machines X. Li, S. Wang, Z.Y. Dong, Lecture Notes in Computer Science 2005 Springer-Verlag Berlin Heidelberg Heidelberg, Germany 696 703
26. X.G. Hua, Y.Q. Ni, J.M. Ko, and K.Y. Wong Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique Journal of Computing in Civil Engineering 21 2 2007 122 135
27. X. Jin, Y. Zhang, and D. Yao Simultaneously prediction of network traffic flow based on PCA-SVR D. Liu, S. Fei, Z. Hou, H. Zhang, C. Sun, Lecture Notes in Computer Science 2007 Springer-Verlag Berlin Heidelberg Heidelberg, Germany 1022 1031
28. X. Liu, H.C. Chen, T.A. Liu, Y. Li, Z.R. Lu, and W.C. Lu Application of PCA-SVR to NIR prediction model for tobacco chemical composition Spectroscopy and Spectral Analysis 27 12 2007 2460 2463
29. I. Seo, B. Ha, S. Lee, C. Shin, J. Lee, and S. Kim Detection of sensor degradation using K-means clustering and support vector regression in nuclear power plant Nuclear Science and Technology 1 2011 312 315
30. G.P. Zhang Neural networks for classification: a survey IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews 30 4 2000 451 462
31. C.-F. Tsai Feature selection in bankruptcy prediction Knowledge-Based Systems 22 2 2009 120 127
32. D. Gil, and M. Johnsson Using support vector machines in diagnoses of urological dysfunctions Expert Systems with Applications 37 6 2010 4713 4718
33. G.H. Dunteman Principal component analysis Quantitative Applications in the Social Science Series vol. 69 1989 Sage Publications Thousand Oaks, CA
34. C.-J. Lu, and Y.-W. Wang Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting International Journal of Production Economics 128 2 2010 603 613
35. F. Boukouvala, F.J. Muzzio, and M.G. Ierapetritou Predictive modeling of pharmaceutical processes with missing and noisy data AICHE Journal 56 11 2010 2860 2872
36. W. Pedrycz, D.J. Lee, and N.J. Pizzi Representation and classification of high-dimensional biomedical spectral data Pattern Analysis and Applications 13 4 2010 423 436
37. M. Traveria, A. Escribano, and P. Palomo Statistical wind forecast for Reus airport Meteorological Applications 17 4 2010 485 495
38. K. Neshatian, and M. Zhang Genetic programming for performance improvement and dimensionality reduction of classification problems Proc. IEEE Congr. Evol. Comput., Hong Kong, China 2008 2811 2818
39. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. fisherfaces: recognition using class specific linear projection IEEE Transactions on Pattern Analysis and Machine Intelligence 19 7 1997 711 720
40. H. Lin, J. Zhao, L. Sun, Q. Chen, and F. Zhou Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis Innovative Food Science and Emerging Technologies 12 2 2011 182 186
41. B. Schölkopf, C.J.C. Burges, and A.J. Smola Advances in Kernel Methods: Support Vector Learning 1999 MIT Press Cambridge, MA
42. W. Wang, Z. Xu, and J.W. Lu Three improved neural network models for air quality forecasting Engineering Computations. International Journal for Computer-Aided engineering 20 2 2003 192 210
43. C.-W. Hsu, C.-C. Chang, and C.-J. Lin A practical guide to support vector classification Technical Report 2003 National Taiwan University Taipei, Taiwan
44. J. Wang, X. Wu, and C. Zhang Support vector machines based on K-means clustering for real-time business intelligence systems International Journal of Business Intelligence and Data Mining 1 1 2005 54 64
45. S.L. Salzberg On comparing classifiers: pitfalls to avoid and a recommended approach Data Mining and Knowledge Discovery 1 3 1997 317 328
46. CII Project definition rating index (PDRI), building projects Implementation Resource 155-2 1999 CII Austin, TX
47. E.A. Gamez, and A. Touran A quantitative analysis of the performance of transportation projects in developing countries Transport Reviews 30 3 2010 361 387
48. F.M. Arain, and S.P. Low Value management through a knowledge-based decision support system for managing variations in educational building projects The International Journal of Construction Management 6 2 2006 81 96
49. Y. Haitovsky Missing data in regression analysis Journal of the Royal Statistical Society: Series B: Methodological 30 1 1968 67 82
50. J. Van Hulse, and T.M. Khoshgoftaar A comprehensive empirical evaluation of missing value imputation in noisy software measurement data The Journal of Systems and Software 81 5 2008 691 708
51. S. Fan, J.R. Liao, R. Yokoyama, L. Chen, and W.-J. Lee Forecasting the wind generation using a two-stage network based on meteorological information IEEE Transactions on Energy Conversion 24 2 2009 474 482
52. Y.F. Wen, C.Z. Cai, X.H. Liu, J.F. Pei, X.J. Zhu, and T.T. Xiao Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression Corrosion Science 51 2 2009 349 355
53. S. Makridakis Accuracy measures: theoretical and practical concerns International Journal of Forecasting 9 4 1993 527 529
54. C.D. Lewis International and Business Forecasting Methods 1982 Butterworths London, UK