针对特定类别的反事实进行自适应性样本排斥,以解释不平衡的分类
Yu Hao1, Xin Gao1, Xinping Diao2
1School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
概括
这项研究引入了一种新的不平衡分类框架,可以在重叠的特征空间中提高模型性能. 该方法适应性地将样本与特定类别的反事实相反,提高分类准确性和模型可信度.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 不平衡的分类在复杂的特征空间和重叠的样本区域中提出了挑战.
- 现有的方法往往无法深入模拟特征标签关系或提供实例级解释.
- 这限制了对分类性能和模型可信度的改进.
研究的目的:
- 提出一个可解释的不平衡分类框架 (CSCF-SR),以动态调节特征空间分布.
- 在使用反事实样本生成解释和分类决策之间形成一个闭环.
- 提高重叠区域样本的模型分类能力.
主要方法:
- 一个类特定的双演员增强学习架构,用于反事实搜索.
- 一个多步骤的动态扰动机制,用于精确的反事实样本生成.
- 适应性样本排斥利用位移向量来澄清类界限.
主要成果:
- 在50个数据集中,CSCF-SR在F1分数和G-平均值上的27种不平衡分类方法中表现出卓越的表现.
- 在25个具有严重类重叠的数据集上观察到显著的改善.
- 该框架有效地提高了重叠区域内的样本的分类.
结论:
- 拟议的CSCF-SR框架通过整合可解释性和适应性样本操纵,为不平衡的分类提供了一种新的方法.
- 该方法显示了显著的性能增长,特别是在具有挑战性的场景和高类重叠的情况下.
- 这项工作有助于更可靠,更准确的不平衡分类模型.
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