构造性混合决策树  

Constructive Hybrid Decision Tree

作　　者：周志华[1] 葛翔[1] 陈兆乾[1] 

机构地区：[1]南京大学计算机软件新技术国家重点实验室,南京210093

出　　处：《计算机学报》2001年第10期1057-1063,共7页Chinese Journal of Computers

基　　金：国家自然科学基金 ( 6 9875 0 0 6 );江苏省自然科学基金 ( BK990 36 )资助

摘　　要：提出了一种构造性混合决策树学习方法 CHDT.该方法用符号学习来进行定性分析 ,用神经学习进行后续的定量分析 ,在一定程度上模拟了人类的思维过程 .CHDT采用了一种独特的构造性归纳机制 ,较好地解决了在缺乏领域知识指导的情况下进行构造性学习的问题 .它通过采用 FTART2网络和适宜于混合决策树的神经网络嵌入机制 ,获得了较强的泛化能力 .实验结果表明 ,CHDT能构造出结构简洁。A constructive hybrid decision tree learning algorithm named CHDT is proposed. CHDT utilizes symbolic learning to perform qualitative analysis and utilizes neural learning to perform following quantitative analysis, which simulates human reasoning process in some sense. CHDT tries to process the instances with pure symbolic decision tree in an instance space defined by only category attributes. Only when the instances cannot be processed, it resorts to neural networks in an instance space defined by continuous attributes. The neural learning algorithm employed by CHDT is FTART2, which is a field theory based adaptive resonance theory model. CHDT virtually embeds neural networks in the leaves of the decision tree. That is, CHDT marks the neural nodes in the learning process and trains only one neural network for all the marked nodes. CHDT employs a unique constructive induction mechanism. It uses a binary logic OR operator to construct new attributes through observing the topology of the trained decision tree. The construction process is repeatedly executed until the accuracy of a new tree is not better than an old tree. Experiments on three UCI data sets and a real-world data set show that CHDT can generate accurate and concise hybrid decision trees, where the accuracy profits from hybrid learning while the conciseness profits from constructive induction.

关 键 词：机器学习 神经网络 决策树 构造性归纳 混合学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]