Research on Investment Risk Assessment of Eco-Materials Industry

Abstract:

With the rapidly deteriorating of ecological environment and depletion of resources, construction investment of eco-materials industry is gradually increasing , so the investment risk assessment has become a hot research problem at present. In this paper, a new investment risk assessment system for eco-materials industry is presented, which combines rough set approach and support vector machine (SVM). It is different from traditional statistical methods. We can get reduced information table by rough set, which implies that the number of index and qualitative variables is reduced with no information loss by rough set approach. And then, this reduced information is used to develop classification rules, and SVM is trained to infer appropriate parameters. The result of the positive research indicated that this system is very valid for investment risk assessment of eco-materials industry and it will have a good application prospect in this area.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 1018-1021
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.1018
• Online since: August 2013
• Authors: Hong Xia Jin, He Ping Yao, Jie Yu
• Keywords: Eco-Materials Industry, Risk Assessment, Rough Set (RS), Support Vector Machine (SVM), Venture Capital

References

[1] Nie Zuoren: Ecomaterials for Circular Society Materials China. 2009, 5, Vol. 28, No. 5.

[2] Zhang Zhi-ping, Wang Ang: Ecological Environment Material Study Research Development. Ecological Environment, 2003(1): 12-15.

[3] Li Aimin, Sun Kangning: Development of Ecomaterials and Their Influence on Society. Silicate Bulletin. 2003, 5.

[4] Wang Cheng, Shi Huisheng: Ecomaterials and Sustainable Development. China Non-metallic Minerals Industry Guide. 2009, 6.

[5] Carl Gold, Peter Sollish: Model selection for support vector machine classification. Neurcomputing. 2005, 55: 221-249.

[6] Gawrys M, Sienkiekiewicz J: Rough set library user's manual. Institute of Computer Science Warsaw University of Technology. (1993).

[7] R. Li, Z. -o. Wang: Mining classification rules using rough sets and neural networks. European Journal of Operational Research. 7(157): 439–448.

DOI: 10.1016/s0377-2217(03)00422-3

[8] Pawlak: Rough sets and intelligent data analysis. Information Science. 2002, 11(147): 1-12.

[9] Dayan Manohar Sinalingam, Narenkumar Pandia: Minmal classification method with eorror correlation codes for multiclass recognization. International Journal of Pattern Recognization and artificial intelligence. 2005, 5: 663-680.