.png){kind=icon}
返回
A Feature Selection Based Serial SVM Ensemble Classifier
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2917310.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A serial support vector machine (SVM) ensemble classifier based on feature selection is designed to improve the efficiency and accuracy of unbalanced data classification so as to fully excavate the potential of features. The performance measurement indicators of the traditional SVM model ``one-versusothers'' are defined. With the goal of reaching higher classification precision and smaller feature set scale, the serial ensemble classifier is built using ``one-versus-others'' binary SVM classifier as a base classifier, and ant colony optimization (ACO) is used to determine the recognizable category and input feature subset of each level. Several experiments on vibration signals of real states of the engine are performed to contrast the classifying effects of the proposed approach, the traditional ``one-versus-one'' and ``one-versus-others'' parallel SVM classifiers, and the results demonstrate the superiority of the proposed approach.
Keyword:
Support vector machine
one-versus-others
unbalanced data classification
feature selection
serial SVM ensemble classifier
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
Integration of classifier diversity measures for feature selection-based classifier ensemble reduction集成分类器多样性度量，用于基于特征选择的分类器集成约简.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷RSC Advances
RSC AdvancesIF0
PREAI
分享收藏A robust fuzzy least squares twin support vector machine for class imbalance learning用于类不平衡学习的鲁棒模糊最小二乘孪生支持向量机
PREAI
分享收藏分享收藏Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big dataMapReduce框架下基于代价敏感语言模糊规则的不平衡大数据分类系统
PREAI
分享收藏
查看更多内容