.png){kind=icon}
返回
Fuzzy-Rough Cognitive Networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2017.08.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the rough information granules are built. In this paper, we cast the RCN model within the framework of fuzzy rough sets in an attempt to eliminate the need for a user-specified similarity threshold while retaining the model's discriminatory power. As far as we know, this is the first study that brings fuzzy sets into the domain of rough cognitive mapping. Numerical results in the presence of 140 well-known pattern classification problems reveal that our approach, referred to as Fuzzy-Rough Cognitive Networks, is capable of outperforming most traditional classifiers used for benchmarking purposes. Furthermore, we explore the impact of using different heterogeneous distance functions and fuzzy operators over the performance of our granular neural network. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Fuzzy cognitive maps
Fuzzy rough sets
Rough cognitive mapping
Pattern classification
Granular classifiers
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:
6.3
论文数:
7.8K
被引数:
3.0W
机构
引用论文
.png){kind=icon}
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Prevalence of urinary incontinence in community dwelling men: a cross sectional nationwide epidemiological survey社区男性尿失禁患病率: 一项全国性横断面流行病学调查
PREAI
分享收藏分享收藏
分享收藏
查看更多内容