提出轻量级分类器，实时提供可信度区间，适合医疗设备等安全场景。
Computationally lightweight classifiers with frequentist bounds on predictions
- 基于Nadaraya-Watson估计器设计，计算复杂度仅O(n)或O(log n)
- 在心电图数据上实现96%以上准确率并给出可解释的置信区间
- 适用于资源受限的实时诊断系统，如植入式医疗设备
尽管传统与神经网络分类器可实现高精度，但无法提供预测的不确定性边界，难以用于安全关键场景。现有基于核的方法虽能提供此类边界，但时间复杂度高达O(n³)，对大规模数据不适用。为此，我们提出一种基于Nadaraya-Watson估计器的新分类算法，并为其预测推导出频率学意义上的不确定性区间。我们在合成数据和MIT-BIH心律失常数据库的心跳信号上进行了评估，结果表明该方法在O(n)和O(log n)操作下实现了超过96%的准确率，同时提供可操作的不确定性边界。这些边界可用于标记低置信度预测，适用于诊断监测等资源受限的实时场景。
原文摘要 · Abstract (English)
While both classical and neural network classifiers can achieve high accuracy, they fall short on offering uncertainty bounds on their predictions, making them unfit for safety-critical applications. Existing kernel-based classifiers that provide such bounds scale with $\mathcal O (n^{\sim3})$ in time, making them computationally intractable for large datasets. To address this, we propose a novel, computationally efficient classification algorithm based on the Nadaraya-Watson estimator, for whose estimates we derive frequentist uncertainty intervals. We evaluate our classifier on synthetically generated data and on electrocardiographic heartbeat signals from the MIT-BIH Arrhythmia database. We show that the method achieves competitive accuracy $>$\SI{96}{\percent} at $\mathcal O(n)$ and $\mathcal O(\log n)$ operations, while providing actionable uncertainty bounds. These bounds can, e.g., aid in flagging low-confidence predictions, making them suitable for real-time settings with resource constraints, such as diagnostic monitoring or implantable devices.
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