基于决策树组合分类器的气温预测

Prediction of Atmospheric Temperature Based on Multi-classifiers of Decision Tree

气象数据挖掘是近年来研究的热点,组合分类器能够实现协同计算以提高效率和准确性,就此本文采用数据挖掘方法中的决策树组合分类器对某地气象进行了气温预测,主要依据C4.5经典算法、Bagging集成方法构建组合决策树,并加入协同的思想建立了预测气温的决策树协同分析模型.实验表明,基于Bagging的决策树协同模型对于局部区域的气温预测具有较高的准确率.

Meteorological data mining has been a hot research spot recently. Combined classifiers can be used in collaborative computing to improve the efficiency and accuracy. Based on C4. 5 classic algorithm and the bagging integrated method,it constructed a cooperative decision tree,and proposed a decision tree model for multi-classifiers to predict the air temperature. Experimental results show that the cooperative model for the prediction of local area atmospheric temperature,based on multi-classifiers of the decision tree,has higher accuracy than others.