The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the ...The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.展开更多
Unification is both necessary and challenging for studying atmospheric particle systems, which are polydispersesystems containing particles of different sizes and shapes. A general framework is proposed to realize the...Unification is both necessary and challenging for studying atmospheric particle systems, which are polydispersesystems containing particles of different sizes and shapes. A general framework is proposed to realize the first order generalization. Within this generalized framework, (1) atmospheric particle shapes are unified into self-similar fractals; (2) a self-similar particle is characterized by various power-law relationships; (3) by combining these power-law relationships for a single particle with Shannon's maximum entropy principle and some concepts in statistical mechanics, unified maximum likelihoood number size distributions are of the Weibull form for atmospheric particle systems. Frontier disciplines (e. g., scaling,fractal,chaos and hierarchy) are argued to provide potential 'tools' for such unification. Several new topics are raised for future research.展开更多
The likelihood function plays a central role in statistical analysis in relation to information, from both frequentist and Bayesian perspectives. In large samples several new properties of the likelihood in relation t...The likelihood function plays a central role in statistical analysis in relation to information, from both frequentist and Bayesian perspectives. In large samples several new properties of the likelihood in relation to information are developed here. The Arrow-Pratt absolute risk aversion measure is shown to be related to the Cramer-Rao Information bound. The derivative of the log-likelihood function is seen to provide a measure of information related stability for the Bayesian posterior density. As well, information similar prior densities can be defined reflecting the central role of likelihood in the Bayes learning paradigm.展开更多
基金Science Research Project of Gansu Provincial Transportation Department(No.2017-012)
文摘The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.
文摘Unification is both necessary and challenging for studying atmospheric particle systems, which are polydispersesystems containing particles of different sizes and shapes. A general framework is proposed to realize the first order generalization. Within this generalized framework, (1) atmospheric particle shapes are unified into self-similar fractals; (2) a self-similar particle is characterized by various power-law relationships; (3) by combining these power-law relationships for a single particle with Shannon's maximum entropy principle and some concepts in statistical mechanics, unified maximum likelihoood number size distributions are of the Weibull form for atmospheric particle systems. Frontier disciplines (e. g., scaling,fractal,chaos and hierarchy) are argued to provide potential 'tools' for such unification. Several new topics are raised for future research.
文摘The likelihood function plays a central role in statistical analysis in relation to information, from both frequentist and Bayesian perspectives. In large samples several new properties of the likelihood in relation to information are developed here. The Arrow-Pratt absolute risk aversion measure is shown to be related to the Cramer-Rao Information bound. The derivative of the log-likelihood function is seen to provide a measure of information related stability for the Bayesian posterior density. As well, information similar prior densities can be defined reflecting the central role of likelihood in the Bayes learning paradigm.
