School of Civil Engineering, Hebei University of Engineering, Handan, P.R.China

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Abstract—In order to evaluate and forecast the general contracting risk, a multi-resolution approach for the price determination of real estate was present in this paper. Real samples have been classified using the novel multi-classifier, namely, support vector machine among which genetic algorithm (GA) is used to determine free parameters of support vector machine. Effects of different sampling approach, kernel functions, and parameter settings used for SVM classification are thoroughly evaluated and discussed. The experimental results indicate that the SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

Index Terms—Support vector machines; principal component analysis, genetic algorithm, risk forecasting, general contracting.

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Cite: Huawang Shi, "Applying Principal Component Analysis, Genetic Algorithm and Support Vector Machine for Risk Forecasting of General Contracting," Journal of Computers vol. 7, no. 1, pp. 301-307, 2012.