Improves on the classification and regression trees technology , increases it ' s classification precision 对分类回归树数据挖掘技术进行了改进,使之具有更高的分类精度。
The thesis combines generalized computing theory with classification and regression trees technology , makes the great theory innovation 本文把广义计算理论和数据挖掘技术相结合,具有很强的理论创新意义。
Combines multi - rules neural network with classification and regression trees technology based on generalized computing theory , implements the abnormal customers recognition system 基于广义计算思想,把多准则神经网络和分类回归树技术相结合,实现异动客浙江大学硕士学位沦义缀户识别系统。
Based on the generalized computing theory , the thesis combines multi - rules neural network with a kind of decision tree - classification and regression trees . further more , we put forward a new kind of abnormal customers recognition model 为进行客户关系管理,本文基于广义计算思想,将多准则神经网络和一种决策树? ?分类回归树相结合,提出了一种新的异动客户识别模型。
The model can improve classification precision and recognition efficiency effectively , make full use of the advantages of multi - rules neural network and classification and regression trees , and make up their respective disadvantages at a certain extent 该模型能够有效提高分类精度和识别效率,充分利用多准则神经网络和分类回归树各自的优点,一定程度上避免各自的缺陷。