THE INVESTING RISK COMPREHENSIVE EVALUATION USING AN IMPROVED SUPPORT VECTOR MACHINE ALGORITHM

摘要：

The electric power projects face the uncertain external environment, they are complex of projects themselves and the ability of the designers, erectors and operators are limited, which make the investing risk evaluation of electric power project becomes a pressing settlement problem. To evaluate the investing risk scientifically and accurately, this paper proposes the multi-level classification evaluating model based on improved support vector machine (SVM), which uses the SVM classification combination in series and introduces the type weight factor and sample weight factor. The model not only solves the shortcomings of small sample, high dimension, nonlinear and local minima in the traditional model, but solves the wrong classification question caused by the number imbalance of training samples and data interference. The investment risk evaluating results of 14 electric power projects in National Power Company show that the model is simple, feasible, and improve the evaluating accuracy and efficiency.

关键词： Support vector machine Investing risk Comprehensive evaluating Electric power projects

作者: ZHI-BIN LIU PENG SHEN

作者单位: Economics and Management Department, North China Electric Power University, Baoding 071003, China College of Economics and Trade, Agricultural University of Hebei, Baoding 071001, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1484-1488

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）