证明规范化相互信息有偏见的行为
Amin Mahmoudi1, Dariusz Jemielniak2
1Management in Networked and Digital Societies (MINDS) Department, Kozminski University, Warsaw, Poland. amahmoudi@kozminski.edu.pl.
Scientific reports
|April 19, 2024
概括
规范化相互信息 (NMI) 随着社区数量的增加而表现出偏见,使其不适合评估聚类和社区检测算法. 这项研究在数学上证明了NMI.
科学领域:
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
- 信息理论 信息理论
背景情况:
- 规范化相互信息 (NMI) 是评估集群和社区检测的标准指标.
- 以前的研究已经观察到NMI的偏见行为随着社区人数的增加,但缺乏正式的证据.
- 在理解NMI在算法评估中偏差的原因方面存在差距.
研究的目的:
- 为了正式证明NMI随着社区数量的增加而产生偏见的行为.
- 确定导致NMI偏差的因果因素.
- 确定NMI对于评估聚类和社区检测算法的不适合性.
主要方法:
- 数学分析和形式证明.
- 调查NMI在社区数量方面的表现.
- 检查对数函数在基于的指标中的对数函数的影响.
主要成果:
- 随着社区数量的增加,NMI的明显偏见得到了证明.
- 为NMI的偏见行为提供了数学证明.
- 该研究发现了使用对数函数的基于的指标的漏洞.
结论:
- 由于其固有的偏见,NMI不适合评估聚类和社区检测算法.
- 数学证明证实了在多个社区的场景中,该指标的局限性.
- 采用对数函数的基于值的指标容易受到类似偏差的影响.
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