.png){kind=icon}
返回
Mining axiomatic fuzzy set association rules for classification problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2011.04.022.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Data mining
Fuzzy association rules
AFS fuzzy logic
Knowledge acquisition
Classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
Fuzzy feature selection based on min-max learning rule and extension matrix.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Electrical Conductivity of Reproductive Tissue for Detection of Estrus in Dairy Cows用于检测奶牛发情的繁殖组织电导率
OAAI
OAAI分享收藏分享收藏
分享收藏分享收藏
查看更多内容