Conformal Rule-Based Multi-label Classification
Conformal Rule-Based Multi-label Classification
复制标题
基于共形规则的多标签分类
DOI：
10.1007/978-3-030-58285-2_25
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Loza Mencía
中科院分区：
文献类型：
--
作者：
Hüllermeier;Fürnkranz;Loza Mencía
我们提倡使用保形预测（CP）来增强基于规则的多标签分类（MLC）。我们特别强调了 CP 和规则学习的互惠互利：规则能够提供 CP 所需的自然（非）一致性分数，而 CP 则提出了一种校准候选规则评估的方法，从而支持更好的预测和更精细的决策。我们在惰性多标签规则学习的案例研究中说明了校准一致性分数的潜在用处。
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.
影响因子：
7.5
作者：
Dembczynski, Krzysztof;Waegeman, Willem;Huellermeier, Eyke
通讯作者：
Huellermeier, Eyke