.png){kind=icon}
返回
Data mining of Bayesian networks using cooperative coevolution
.png){kind=icon}
PRE
AI
DOI：10.1016/S0167-9236(03)00115-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes a novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data. A Bayesian network is a graphical knowledge representation tool. However, learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel algorithm that combines the characteristics of these approaches to improve learning effectiveness and efficiency. The new learning algorithm consists of the conditional independence (CI) test and the search phases. In the Cl test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian networks are generated by a cooperative coevolution genetic algorithm (GA). We conduct a number of experiments and compare the new algorithm with our previous algorithm, Minimum Description Length and Evolutionary Programming (MDLEP), which uses evolutionary programming (EP) for network learning. The results illustrate that the new algorithm has better performance. We apply the algorithm to a large real-world data set and compare the performance of the discovered Bayesian networks with that of the back-propagation neural networks and the logistic regression models. This study illustrates that the algorithm is a promising alternative to other data mining algorithms. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
cooperative coevolution
evolutionary computation
data mining
Bayesian networks
期刊
IF:
6.8
论文数:
3.8K
被引数:
1.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
The Northern Sea Routes and Korea's Trade with Europe: Implications for Korea's Shipping Industry
OAAI
OAAI分享收藏Asymptotics of reaction–diffusion fronts with one static and one diffusing reactant具有一个静态和一个扩散反应物的反应扩散前沿的渐近性OAAI
OAAI分享收藏Studies on flavonoid metabolism. Identification of the metabolities of (+)-catechin in rat urineOAAI
OAAI分享收藏分享收藏
查看更多内容