Risk Assessment Model Based on SVDD and Fuzzy Regression Method

Abstract:

The paper aims to solve the problem of insufficient high risk data in risk assessment of R&D projects. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent risk assessment model based on SVDD with fuzzy regression information is also proposed. The model comes into being a new approach. Applying this approach, firstly verify the conversional risk evaluation indexes by fuzzy regression technique to develop a sensitive index system. Secondly the study uses the historical risk data referring to these indexes to train the SVDD one-class classifier. Unlike previously proposed intelligent methods of risk assessment, with this model the risk level can be distinguished only by training of low risk data. The results of its application on an example show that the method is feasible for risk assessment with the fuzzy high risk data.

• Periodical: Applied Mechanics and Materials (Volumes 58-60)
• Pages: 1807-1812
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.58-60.1807
• Online since: June 2011
• Authors: Hui Li Zhou, Wen Bing Chang, Sheng Han Zhou
• Keywords: Fuzzy Regression, Risk Assessment, Support Vector Data Description

References

[1] Liu Yanling, Construction of project risk assessment system, Construction Management Modernization, vol. 73, no. 4, pp.25-29, (2003).

[2] Wang wei, Research on project risk evaluation and prediction based on statistical learning theory, Tianjin: Tianjin Unversity, (2008).

[3] D. M. J. Tax and R.P.W. Duin, Support vector domain description, Pattern Recognition Letters, vol. 20, no. 14, pp.1191-1199, (1999).

DOI: 10.1016/s0167-8655(99)00087-2

[4] D. M. J. Tax and R.P.W. Duin, Support vector data description, Machine Learning, vol. 54, no. 1, pp.45-66, Jan. (2004).

DOI: 10.1023/b:mach.0000008084.60811.49

[5] N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines, Cambridge University Press, (2000).

[6] V. N. Vapnik, Statistical learning theory, New York: Wiley, (1998).

[7] W. Sakla, A. Chan, J. Ji, A. Sakla, a SVDD-based algorithm for target detection in hyperspectral imagery, IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 2, pp.384-388, march (2011).

DOI: 10.1109/lgrs.2010.2078795

[8] Li lingjun, Zhang zhousuo, He zhengjia, Research of mechanical system fault diagnosis based on support vector data description, " Journal of Xi, an Jiaotong University, vol. 37, no. 9, pp.910-913, sep. (2003).

[9] Wang Zhongyu, Wu bolin, Statistics of fuzzy data, Harbin Institute of Technology Press, (2008).