摘要
在机器学习的分类问题研究中,对分类学习算法的正确评价是非常重要的。现实中,许多性能度量指标被从不同的角度提出,文中主要介绍了基于错误率的、基于混淆矩阵的和基于统计显著性检验的三大类性能度量指标,详细地讨论了分类学习算法各性能度量指标的提出背景、意义以及适用范围,分析了各种性能度量之间的差异,提出和分析了各方法中有待进一步研究的问题和方向。进一步,通过实验数据横向(每类度量中各方法之间的类内差异)和纵向(3类度量之间的类间差异)对照了各性能度量指标之间的差异,分析了各性能度量指标在分类算法选择上的一致性。
In the research of classification task of machine learning,it is important for correctly evaluating the performance of the learning algorithm.In practical application,many performance measure indexes are proposed based on different perspectives.Three kinds of performance measure indexes based on error rate,confusion matrix and statistical test are introduced in this paper.The background,significance and scope of each measure index are discussed.The differences of different methods are analyzed.The future research problems and directions are also put forward and analyzed.Furthermore,the differences of these performance measure indexes are also compared by experimental data in portrait and landscape.The consistency of these performance measure indexes is also analyzed in classification algorithm selection.
作者
杨杏丽
YANG Xing-li(School of Mathematical Sciences,Shanxi University,Taiyuan 030006,China;School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)
出处
《计算机科学》
CSCD
北大核心
2021年第8期209-219,共11页
Computer Science
基金
国家自然科学基金(62076156,61806115)
山西省应用基础研究项目(201901D111034,201801D211002)
统计与数据科学前沿理论及应用教育部重点实验室开放研究课题(KLATASDS2007)。
关键词
性能度量
错误率
混淆矩阵
统计检验
Performance measure
Error rate
Confusion matrix
Statistical test
