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When using support vector regression to predict building energy consumption, since the energy influence factors are quite abundant and complex, the features associated with the statistical model could be in large quantity. This paper focuses in feature selection for the purpose of reducing model complexity without sacrificing performance. The optimal features are selected by their feasibility of obtaining...
This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit...
A novel support vector regression (SVR) optimized by an integrated particle swarm optimization (PSO) was proposed. The optimization mechanism combined the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the SVR kernel parameter setting. By incorporating two types of PSO, the parameters and the input features of SVR were optimized simultaneously...
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