.png){kind=icon}
返回
A density and connectivity based decision rule for pattern classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2014.08.027.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we propose a novel neighborhood classifier, Surrounding Influence Region (SIR) decision rule. Traditional Nearest Neighbor (NN) classifier is a distance-based method, and it classifies a sample using a predefined number of neighbors. In this study neighbors of a sample are determined using not only the distance, but also the connectivity and density information. One of the well-known proximity graphs, Gabriel Graph, is used for this purpose. The neighborhood is unique for each sample. SIR decision rule is a parameter-free approach. Our experiments with artificial and real data sets show that the performance of the SIR decision rule is superior to the k-NN and Gabriel Graph neighbor (GGN) classifiers in most of the data sets. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Classification
Nearest neighbor
Gabriel Graph
Density
Connectivity
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:
7.5
论文数:
3.0W
被引数:
10.2W
机构
暂无机构信息
引用论文
When right becomes less right: Neural dedifferentiation during suprasegmental speech processing in the aging brain.png)
NeuroImage
NeuroImageIF0
PREAI
分享收藏Breast cancer detection using rank nearest neighbor classification rulesPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容