.png){kind=icon}
返回
KNN-based maximum margin and minimum volume hyper-sphere machine for imbalanced data classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-017-0720-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Imbalanced data classification is often met in our real life. In this paper, a novel k-nearest neighbor (KNN)-based maximum margin and minimum volume hyper-sphere machine (KNN-(MVHM)-V-3) is presented for the imbalanced data classification. The basic idea is to construct two hyper-spheres with different centres and radiuses. The first one contains majority examples and the second one covers minority examples. When constructing the first hyper-sphere, we remove some redundant majority samples using k-nearest neighbor (KNN)-based strategy to balance two classes of samples. Meanwhile, we maximize the margin between two hyper-spheres and minimize their volumes, which can result in two tight boundaries around each class. Similar to the twin hyper-sphere support vector machine (THSVM), KNN-(MVHM)-V-3 solves two related SVM-type problems and avoids the matrix inverse operation when solving the convex optimization problems. KNN-(MVHM)-V-3 considers not only the within-class information but also the between-class margin, then it achieves better performance in comparison with other state-of-the-art algorithms. Experimental results on twenty-five datasets validate the significant advantages of our proposed algorithm.
Keyword:
Maximum margin
Minimum volume
K-nearest neighbor
Hyper-sphere
KNN-(MVHM)-V-3
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:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
An experimental test of density- and distant-dependent recruitment of mahogany (Swietenia macrophylla) in southeastern Amazonia.png)
Oecologia
OecologiaIF0
PREAI
分享收藏An efficient weighted Lagrangian twin support vector machine for imbalanced data classification一种有效的加权拉格朗日孪生支持向量机，用于不平衡数据分类PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容