Paper Title:
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.

  Info
Periodical
Edited by
Qi Luo
Pages
1807-1812
DOI
10.4028/www.scientific.net/AMM.58-60.1807
Citation
H. L. Zhou, W. B. Chang, S. H. Zhou, "Risk Assessment Model Based on SVDD and Fuzzy Regression Method", Applied Mechanics and Materials, Vols. 58-60, pp. 1807-1812, 2011
Online since
June 2011
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Price
$32.00
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