首页 | 本学科首页   官方微博 | 高级检索  
     

一种基于神经网络集成的决策树构造方法
引用本文:苏晓影,贺跃,郑建军.一种基于神经网络集成的决策树构造方法[J].计算机仿真,2006,23(11):95-98.
作者姓名:苏晓影  贺跃  郑建军
作者单位:1. 北京理工大学信息学院计算机系,北京100081
2. 北京理工大学管理与经济学院,北京100081
摘    要:神经网络集成方法具有比单个神经网络更强的泛化能力,却因为其黑箱性而难以理解;决策树算法因为分类结果显示为树型结构而具有良好的可理解性,泛化能力却比不上神经网络集成。该文将这两种算法相结合,提出一种决策树的构造算法:使用神经网络集成来预处理训练样本,使用C4.5算法处理预处理后的样本并生成决策树。该文在UCI数据上比较了神经网络集成方法、决策树C4.5算法和该文算法,实验表明:该算法具有神经网络集成方法的强泛化能力的优点,其泛化能力明显优于C4.5算法;该算法的最终结果昆示为决策树,显然具有良好的可理解性。

关 键 词:神经网络集成  神经网络分类器  决策树
文章编号:1006-9348(2006)11-0095-04
收稿时间:2005-09-14
修稿时间:2005年9月14日

A Decision Tree Algorithm Based on Neural Network Ensemble
SU Xiao-ying,HE Yue,ZHENG Jian-jun.A Decision Tree Algorithm Based on Neural Network Ensemble[J].Computer Simulation,2006,23(11):95-98.
Authors:SU Xiao-ying  HE Yue  ZHENG Jian-jun
Affiliation:1. School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China; 2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Abstract:Neural network ensemble is with stronger generalization ability compared with a single neural network.But the ensemble is lack of comprehensibility because it is regarded as a 'black box'.And decision tree is with good comprehensibility.But its generalization ability can not be compared with neural network ensemble.In this paper,an algorithm for building a decision tree is proposed which combines the merits of both the neural network ensemble and the decision tree.The algorithm uses neural network ensemble to reprocess the training set then forms a C4.5 decision tree.Experimental results are compared among neural network ensemble,decision tree and the algorithm introduced in this paper.Experiments show that the algorithm in this paper is with strong generalization ability inherited from neural network ensemble and it has stronger generalization than C4.5 decision tree.Because the result of the algorithm is shown as a tree,the algorithm has good comprehensibility.
Keywords:Neural network ensemble  Neural network classifiers  Decision tree
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号