论文标题
基于共形规则的多标签分类
Conformal Rule-Based Multi-label Classification
论文作者
论文摘要
我们主张使用共形预测（CP）来增强基于规则的多标签分类（MLC）。特别是，我们强调了CP和规则学习的共同利益：规则具有提供自然的（非）合规得分的能力，而CP则需要，而CP提出了一种校准评估候选规则的方法，从而支持更好的预测和更详尽的决策。我们说明了有关懒惰多标签规则学习的案例研究中校准合规得分的潜在实用性。
We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.
