Decision making for contractor insurance deductible using the evolutionary support vector machines inference model

被引:9
作者
Cheng, Min-Yuan [1 ]
Peng, Hsien-Sheng [2 ]
Wu, Yu-Wei [1 ]
Liao, Yi-Hung [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Construct Engn, Taipei, Taiwan
[2] Natl Taiwan Univ Sci & Technol, Ecol & Hazard Mitigat Engn Res Ctr, Taipei, Taiwan
关键词
Loss frequency; Loss severity; Construction insurance; Deductible decision;
D O I
10.1016/j.eswa.2010.11.084
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Loss risk during the course of a construction project may be described in terms of frequency (i.e., loss frequency) and severity (i.e., loss severity). This study focused on improving the methodology used to evaluate loss risk. The authors first identified the common attributes of building construction project loss through a review of the literature and interviews with experts. Objective factors adequate to describe loss attributes were selected as model inputs. The loss prediction model was created using the evolutionary support vector machine inference model (ESIM) and deployed to evaluate loss frequency and loss severity. This research combined the deductible efficient frontier curve with the indifference curve of risk versus insurance cost, and developed criteria for optimal insurance deductible decision making. (c) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:6547 / 6555
页数:9
相关论文
共 9 条
[1]   Training ν-support vector classifiers:: Theory and algorithms [J].
Chang, CC ;
Lin, CJ .
NEURAL COMPUTATION, 2001, 13 (09) :2119-2147
[2]  
Day R, 2002, ICCN 2002: INTERNATIONAL CONFERENCE ON COMPUTATIONAL NANOSCIENCE AND NANOTECHNOLOGY, P36
[3]  
Drucker H, 1997, ADV NEUR IN, V9, P155
[4]   Integrating fmGA and CYCLONE to optimize the schedule of dispatching RMC trucks [J].
Feng, CW ;
Wu, HT .
AUTOMATION IN CONSTRUCTION, 2006, 15 (02) :186-199
[5]  
GOLDBERG DE, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P56
[6]   A simple decomposition method for support vector machines [J].
Hsu, CW ;
Lin, CJ .
MACHINE LEARNING, 2002, 46 (1-3) :291-314
[7]   A GA-based feature selection and parameters optimization for support vector machines [J].
Huang, Cheng-Lung ;
Wang, Chieh-Jen .
EXPERT SYSTEMS WITH APPLICATIONS, 2006, 31 (02) :231-240
[8]  
KNJAZEW D, 2003, COMPETENT GENETIC AL
[9]  
LIN CF, 2004, THESIS NATL TAIWAN U