修复不平衡的二进制分类:一个不对称的贝叶斯式学习方法
Letícia F M Reis1, Diego C Nascimento2, Paulo H Ferreira3
1Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, São Paulo, Brazil.
PloS one
|October 16, 2024
概括
新的不对称罗马克斯分布模型改善了对不平衡数据的二进制分类. 这些贝叶斯函数在现实应用中优于传统方法,如物流回归.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 计算统计学 计算统计学
背景情况:
- 标准的二进制分类模型假定数据是平衡的,这可能导致不平衡数据集的性能差和偏差.
- 传统的对称链接函数 (例如,logit,probit) 可能无法有效处理具有偏斜数据分布的分类任务.
研究的目的:
- 介绍基于罗马克斯分布及其变体的二进制回归的新型不对称链接函数.
- 评估这些新功能在解决分类中的数据不平衡挑战方面的表现.
主要方法:
- 开发了新的贝叶斯不对称分类函数,使用洛马克斯分布变量 (权力,反向).
- 在R工作流中使用Stan实现了这些函数,用于贝叶斯推理.
- 使用现实世界的不平衡数据集,将拟议的模型与经典对称链接函数进行了比较.
主要成果:
- 拟议的非对称的罗马克斯函数在二进制分类任务中表现出优于传统链接函数的性能.
- 具体来说,反向功率双 Lomax (RPDLomax) 模型有效地区分了不平衡数据中的失败和成功概率.
- 与物流回归 (36.0%的失败,39.5%的成功) 不同的是,RPDLomomax的失败概率较低 (21.4%),成功概率较高 (63.7%).
结论:
- 拟议的非对称的罗马克斯方法为二进制数据分类提供了具有竞争力和有效的替代方案,特别是在数据集不平衡的情况下.
- 这些新型函数与标准后勤回归相比,可以更好地区分类别.
- 在R中贝叶斯的实现方便了实际应用和进一步研究非对称二进制回归.
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