用排序概率评分提升有序分类的置信集可靠性
Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score
- 基于排序概率评分构建非相似性函数，天然反映有序误差严重性
- 生成连续且中位数居中的预测集，宽度与误判严重性平衡更优
- 适用于图像和表格数据，不依赖模型且计算效率高
有序分类在医疗、金融等高风险领域广泛应用，需考虑分类错误的严重程度。传统的分布无关置信集虽有边缘覆盖保证，但效果依赖于非相似性函数的选择。本文提出基于排序概率评分（RPS）的有序分类置信预测方法，该评分是累积预测分布上的合理评分规则，能自然体现有序风险，但在以往有序置信预测中被忽视。以RPS作为非相似性度量时，可构造出中位数居中的连续预测集。该方法无需依赖具体模型，支持评估型与分组型有序类别，且实现效率高于贪婪区间选择。在多个图像与表格数据集上，RPS-based CP产生的预测集具有连续性，并在预测集宽度与有序误覆盖程度之间取得更优平衡。
原文摘要 · Abstract (English)
Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors. Conformal prediction (CP) provides distribution-free prediction sets with marginal coverage guarantees; however, its practical effectiveness depends critically on the choice of nonconformity function. We introduce a CP method for ordinal classification based on the ranked probability score (RPS), a proper scoring rule defined over cumulative predictive distributions. Although it reflects ordinal risk quite naturally, it has largely been neglected in conformal ordinal prediction (COP). When used as a measure of nonconformity, RPS yields median-centered contiguous prediction sets by construction. The method is model-agnostic, supports both assessed and grouped ordered categorical outcomes, and permits efficient implementation compared to greedy interval selection procedures. Across multiple ordinal image and tabular datasets, RPS-based CP produces contiguous prediction sets and strikes a favorable balance between prediction set width and the magnitude of ordinal miscoverage relative to existing CP methods.
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