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In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize...
Concrete carbonation depth forecasting is significant to avoid the cracking of concrete. In the study, support vector regression (SVR) which is the regression model of support vector machine (SVM) is proposed to forecast concrete carbonation depth. Water cement ratio, cement consumption and service time have an important influence on concrete carbonation depth, so they are important features in concrete...
An evaluation method based on support vector regression (SVR) is put forward for the purpose of predicting subjective perceptions of automobile seat comfort. The inputs included fourteen seat interface pressure measures, three anthropometric. The output was an overall comfort index derived from occupant responses to a survey. In process of experimental data analysis, the algorithm of the least squares...
Freight volume forecasting is significant to highway web plan. Here, support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast freight volume. We adopt genetic algorithm (GA) to seek the optimal parameters of SVR in order to improve the efficiency of prediction. The data of freight volume in a certain port from 1998 to 2007 is used as a case study. The experimental results...
In the analysis of predicting financial distress based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone...
There is an increasing interest in more accurate prediction of software maintainability in order to better manage and control software maintenance. Recently, TreeNet has been proposed as a novel advance in data mining that extends and improves the CART (classification and regression trees) model using stochastic gradient boosting. This paper empirically investigates whether the TreeNet model yields...
As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in predicting the total workload in telecommunication. The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of support vector regression(SVRs) which influence...
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