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Proceedings ArticleDOI

Recognizing the Investment Risk of Project Based on PSO and SVM

28 Dec 2009-pp 1-4
TL;DR: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm and Support Vector Machines, and these parameters are used to develop classification rules and train SVM.
Abstract: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.
Citations
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Journal Article
TL;DR: In this paper, the first step to develop the investment risk early warning system of infrastructure project is to design the investmentrisk early-warning index and then, using analytic hierarchy process (APH), the weights of investment risk index at infrastructure project are determined quantificationally.
Abstract: The first step to develop the investment risk early-warning system of infrastructure project is to design the investment risk early-warning index This paper establishes the indicator system of investment risk at infrastructure project Then, using analytic hierarchy process (APH), the weights of investment risk early-warning index at infrastructure project are determined quantificationally, which can be used to determine the investment risk early-warning index

1 citations

References
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Proceedings ArticleDOI
17 Jun 1990
TL;DR: A comparison of the predictive abilities of both the neural network and the discriminant analysis method for bankruptcy prediction shows that neural networks might be applicable to this problem.
Abstract: A neural network model is developed for prediction of bankruptcy, and it is tested using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. The results show that neural networks might be applicable to this problem

767 citations


Additional excerpts

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Journal ArticleDOI
TL;DR: A hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network is proposed, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through roughSet approach.
Abstract: This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2400 Korean firms during the period 1994–1997 were selected, and for the validation, k-fold validation was used.

308 citations

Journal ArticleDOI
TL;DR: One measure of risk is systematic risk, defined in terms of the covariance of a security's return with the return from the market portfolio as mentioned in this paper, which is referred to as beta.
Abstract: The measurement and determination of risk have received considerable attention in recent years. One measure of risk is systematic risk, defined in terms of the covariance of a security's return with the return from the market portfolio. The relationship is often standardized by dividing the covariance by the variance of return from the market portfolio. Hereafter, this measure of standardized systematic risk shall be referred to as beta.

199 citations

Journal ArticleDOI
TL;DR: In this paper, an extension to the case of SVMs with quadratic slack penalties is given and a simple approximation for the evidence is derived, which can be used as a criterion for model selection.

176 citations

Proceedings ArticleDOI
07 Nov 2005
TL;DR: The simulating results show that this modified particle swarm optimization algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively.
Abstract: A modified particle swarm optimization (PSO) algorithm is proposed in this paper to avoid premature convergence with the introduction of mutation operation. The performance of this algorithm is compared to the standard PSO algorithm and experiments indicate that it has better performance with little overhead.

138 citations


Additional excerpts

  • ...( ) 1 y x w b i i ⋅ + ≥ , 1 2 i = , , n (2)...

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