.png){kind=icon}
返回
Fuzzy Granular Hyperplane Classifiers
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3002904.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Granular computing has advantage of knowledge discovery for complex data. In the paper, we present Fuzzy Granular Hyperplane Classifiers (FGHCs) for data classification from a new angle of Granular Computing. First, we introduce a fuzzy granular hyperplane concept by defining fuzzy granule, fuzzy granular vector, metrics and operators. Next, for binary classification problem, we present solving optimal fuzzy granular hyperplane through evolution strategy; the learning algorithm of parameters and the prediction algorithm of instances are also proposed. Finally, a multi-classification prediction model is designed by combining a set of Fuzzy Granular Hyperplane Classifiers based on vote strategy. In order to evaluate performance, we employed 10-fold cross validation to verify on UCI dataset and Alzheimer's Disease Voice dataset. Theoretical analysis and experiments demonstrated that FGHCs have good performance.
Keyword:
Support vector machines
Prediction algorithms
Machine learning
Training
Licenses
Computational modeling
Big Data
Fuzzy granular hyperplane
machine learning
granular computing
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:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Fracture risk with use of liver enzyme inducing antiepileptic drugs in people with active epilepsy: Cohort study using the General Practice Research Database.png)
SeizureOAAI
SeizureIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容