.png){kind=icon}
返回
A new dataset evaluation method based on category overlap
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2010.12.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The quality of dataset has a profound effect on classification accuracy, and there is a clear need for some method to evaluate this quality. In this paper, we propose a new dataset evaluation method using the R-value measure. This proposed method is based on the ratio of overlapping areas among categories in a dataset. A high R-value for a dataset indicates that the dataset contains wide overlapping areas among its categories, and classification accuracy on the dataset may become low. We can use the R-value measure to understand the characteristics of a dataset, the feature selection process, and the proper design of new classifiers. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Feature
Feature selection
R-value
Dataset
Classification
Machine learning algorithm
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:
6.3
论文数:
8.3K
被引数:
3.3W
机构
暂无机构信息
引用论文
Invariant optimal feature selection: A distance discriminant and feature ranking based solution不变最优特征选择: 基于距离判别和特征排序的解决方案.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏Normal Ovarian Function in Subfertile Mouse with Amhr2-Cre-Driven Ablation of Insr and Igf1rGenes
OAAI
GenesIF0
OAAI分享收藏Quantitative approach to hückel rule the relations between the cycles of a molecular graph and the thermodynamic stability of a conjugated moleculeTetrahedron
TetrahedronIF0
PREAI
分享收藏 没有更多内容
没有更多内容