.png){kind=icon}
返回
Imbalanced data classification using second-order cone programming support vector machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2013.11.021.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning from imbalanced data sets is an important machine learning challenge, especially in Support Vector Machines (SVM), where the assumption of equal cost of errors is made and each object is treated independently. Second-order cone programming SVM (SOCP-SVM) studies each class separately instead, providing quite an interesting formulation for the imbalanced classification task. This work presents a novel second-order cone programming (SOCP) formulation, based on the LP-SVM formulation principle: the bound of the VC dimension is loosened properly using the l(infinity)-norm, and the margin is directly maximized using two margin variables associated with each class. A regularization parameter C is considered in order to control the trade-off between the maximization of these two margin variables. The proposed method has the following advantages: it provides better results, since it is specially designed for imbalanced classification, and it reduces computational complexity, since one conic restriction is eliminated. Experiments on benchmark imbalanced data sets demonstrate that our approach accomplishes the best classification performance, compared with the traditional SOCP-SVM formulation and with cost-sensitive formulations for linear SVM. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Class-imbalanced data
Support Vector Machines
LP-SVM
SOCP-SVM
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
A dynamic over-sampling procedure based on sensitivity for multi-class problems.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏Attitudes of University Students Regarding Potential Conflicts in Socially Responsible Companies
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
分享收藏
查看更多内容