.png){kind=icon}
返回
Multivariate comparison of classification performance measures
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2017.12.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The assessment of the classification performance can be based on class indices, such as sensitivity, specificity and precision, which describe the classification results achieved on each modelled class. However, in several situations, it is useful to represent the global classification performance with a single number. Therefore, several measures have been introduced in literature to deal with this assessment, accuracy being the most known and used. These metrics have been proposed to generally face binary classification tasks and can behave differently depending on the classification scenario. In this study, different global measures of classification performances are compared by means of results achieved on an extended set of real multivariate datasets. The systematic comparison is carried out through multivariate analysis. Further investigations are then derived on specific indices to understand how the presence of unbalanced classes and the number of modelled classes can influence their behaviour. Finally, this work introduces a set of benchmark values based on different random classification scenarios. These benchmark thresholds can serve as the initial criterion to accept or reject a classification model on the basis of its performance.
Keyword:
Classification indices
Classification measures
Comparison
Random benchmark
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:
3.8
论文数:
4.6K
被引数:
1.2W
机构
引用论文
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE受试者工作特征 (ROC) 曲线下面积的含义和用途.png)
RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏A simple generalisation of the area under the ROC curve for multiple class classification problems多类分类问题的ROC曲线下面积的简单概括MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏Relationships between apple texture and rheological parameters by means of multivariate analysis
PREAI
分享收藏
查看更多内容