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Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel...
The correct identification of two-phase flow regime is the basis for the accurate measurement of other flow parameters in two-phase flow measurement. A PSO-SVM(Particle Swarm Optimization and Support Vector Machine) model, which can overcome selecting parameters needed in SVM model, was developed to identify the flow regime. The application of PSO-SVM improves the accuracy of flow regime recognition...
Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. This paper proposes an image classifier based on Support Vector Machine which related parameters are optimized by an improved Particle Swarm Optimization (PSO) algorithm. Because control parameters selection of PSO have no corresponding theoretical...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
In this paper, the application of support vector machine (SVM) approach based on the statistics-learning theory of structural risk minimization in heart disease diagnosis. Aiming at the blindness of man made choice of parameter and kernel function of SVM, a chaotic adaptive particle swarm optimization (CAPSO) method is applied to select parameters of SVM in the paper and genetic characteristics of...
To choose an appropriate kernel function is one major task for SVM. Different kernel functions will produce different SVMs and may result in different performances. Combined kernel function shows more stable and higher performance than single kernel function, so there is a need to optimize the combined kernel function to enhance the generalization capability of SVM. This paper proposes to optimize...
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