基于神经网络的分类决策树构造  

A New Approach to Establish Decision Trees for Data Mining Based on Neural Network Learning

作　　者：徐爱琴[1] 张德贤[2] 

机构地区：[1]中国科技大学研究生院,北京100039 [2]河南大学软件工程重点实验室,开封475001

出　　处：《计算机工程与应用》2000年第10期43-45,55,共4页Computer Engineering and Applications

基　　金：河南省自然科学基金资助

摘　　要：目前基于符号处理的方法是解决分类规则提取问题的主要方法,而基于神经网络的连接主义方法则用的不多,其主要原因在于虽然神经网络的分类精度高,但难于提取其所隐含的分类规则与知识.针对这个问题,结合神经网络的具体特点,该文提出了一种基于神经网络的构造分类决策树的新方法.该方法通过神经网络训练建立各属性与分类结果之间的关系,进而通过提取各属性与分类结果之间的导数关系来建立分类决策树.给出了具体的决策树构造算法.同时为了提高神经网络所隐含关系的提取效果,提出了关系强化约束的概念并建立了具体的模型.实际应用结果证明了算法的有效性.： The symbolic approaches currently are the main methods for classification rules mining．But connectionist approaches based on neural networks are not used frequently．The major reason is that although neural networks can give a high precise in classification,it is difficult to acquire the rules and knowledge generated by neural networks．To this problem,this paper presents a new approach to establish decision trees based on neural networks．The main idea of the approach presented is as follows：firstly through neural network learning, the relation model of attributes and classification of the data mining problem is established,then the decision tree is constructed based on analysis the derivative relations of classification and those attributes．The detailed algorithms for establishment of decision tree are presented．To improve the effectiveness of analysis of implicit relations generated by neural networks,the conception and concrete models of relation－enhancing constraints are also presented． The results of practice have proved the effectiveness of this approach．

关 键 词：数据挖掘 分类决策树 神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP301[自动化与计算机技术—控制科学与工程]