Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the ou...Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the outcome of Least Square Regression （LSR）, the result showed that RAGA not only had high accuracy and more effective, but also saved calculating time. The authors provides new effective methods for calculating index of crop water production function.展开更多
In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified fro...In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.展开更多
基金Supported by Science and Technology Research Program of Heilongjiang Province(GB06B106-7)
文摘Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the outcome of Least Square Regression （LSR）, the result showed that RAGA not only had high accuracy and more effective, but also saved calculating time. The authors provides new effective methods for calculating index of crop water production function.
文摘In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.
