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Proceedings ArticleDOI

Project financing risk assessment based on ACO and SVM

29 Sep 2009-Vol. 4, pp 300-302
TL;DR: Based on the combination of ant colony optimization (ACO) and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project by making use of ACO obtaining appropriate parameters.
Abstract: Based on the combination of ant colony optimization(ACO)and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project. By making use of ACO obtaining appropriate parameters we can improve the general recognizing ability of SVM. After that, 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 ArticleDOI
TL;DR: To find the best performing machine learning algorithms to use with Snort so as to improve its detection, the application of combined and optimized MLAs worked quite well.
Abstract: In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and th...

5 citations


Cites methods from "Project financing risk assessment b..."

  • ...The ensemble SVM was also optimized with Ant Colony Optimization (Jinyu and Xin, 2009 and Acevedo et al., 2006)) to select the parameters of SVM automatically and that proved to be effective....

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  • ...Ant Colony Optimization (ACO) is a metaheuristic algorithm that uses the idea exhibited in an ant colony to find the shorted path from a food source to the nest through pheromone information without employing any visual cues....

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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

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


"Project financing risk assessment b..." refers background in this paper

  • ...Subject to i (5) ( ) 1 We can search the nonnegative Lagrange multipliers by solving the following optimization problem,...

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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


"Project financing risk assessment b..." refers background in this paper

  • ...The transition rule is arg max , , ijp Tanslate to the solution of jj else Not (2) Pheromone updating rule: The pheromone updating rule of the solution j is defined as follow: 1 ( 1) ( ) , 1, 2, , m k j j j k t t j n (3) , 1, 2, , 0, k k j jk j k j QL L j n L Where, kj denotes the pheromone increase of the solution j ; kjL denotes the changing value of the objective function; denotes the pheromone maintenance; Q denotes the pheromone intensity that the ant leaves....

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  • ..., ij p Tanslate to the solution of j j else Not (2)...

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