.png){kind=icon}
返回
An efficient instance selection algorithm to reconstruct training set for support vector machine
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2016.10.031.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Support vector machine is a classification model which has been widely used in many nonlinear and high dimensional pattern recognition problems. However, it is inefficient or impracticable to implement support vector machine in dealing with large scale training set due to its computational difficulties as well as the model complexity. In this paper, we study the support vector recognition problem mainly in the context of the reduction methods to reconstruct training set for support vector machine. We focus on the fact of uneven distribution of instances in the vector space to propose an efficient self-adaption instance selection algorithm from the viewpoint of geometry-based method. Also, we conduct an experimental study involving eleven different sizes of datasets from UCI repository for measuring the performance of the proposed algorithm as well as six competitive instance selection algorithms in terms of accuracy, reduction capabilities, and runtime. The extensive experimental results show that the proposed algorithm outperforms most of competitive algorithms due to its high efficiency and efficacy. (C) 2016 The Authors. Published by Elsevier B.V.
Keyword:
Support vector machine
Instance selection
Machine learning
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
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
分享收藏Adaptive pruning algorithm for least squares support vector machine classifier.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Evolutionary-based selection of generalized instances for imbalanced classification基于进化的不平衡分类广义实例选择
PREAI
分享收藏
查看更多内容