.png){kind=icon}
返回
Neural-based learning classifier systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2007.190671.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
UCS is a s (u) under bar pervised learning (c) under bar lassifier (s) under bar ystem that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks ( NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning ( NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.
Keyword:
representations
evolutionary computing and genetic algorithms
neural nets
rule-based processing
data mining
classification
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
A study on the status of fluoride ion in groundwater of coastal hard rock aquifers of south India
PREAI
分享收藏
Cross-linkable Polymer Matrix for Enhanced Thermal Stability of Succinonitrile-based Polymer Electrolyte in Lithium Rechargeable Batteries可交联聚合物基质，用于增强锂可充电电池中基于丁二腈的聚合物电解质的热稳定性
OAAI
OAAI分享收藏
查看更多内容