论文标题
通过汇总交叉验证来选择分类器会在心房颤动定位的小样本分类中更加一致性
Selecting Classifiers by Pooling over Cross-Validation Results in More Consistency in Small-Sample Classification of Atrial Flutter Localization
论文作者
论文摘要
在诊所使用AI时，选择学习机（例如分类器）是一项重要任务。 k折交叉验证是一种实用技术，可以简单地推理此类机器。但是，该食谱会生成许多型号，并且没有提供确定最佳方法的方法。在本文中，提出了修改的配方，该食谱会生成更一致的机器，具有相似的在平均水平的性能，但较少的样本损失差异和较少的特征偏差。通过将食谱应用于心房的颤动定位问题，提供了用例。
Selecting learning machines such as classifiers is an important task when using AI in the clinic. K-fold crossvalidation is a practical technique that allows simple inference of such machines. However, the recipe generates many models and does not provide a means to determine the best one. In this paper, a modified recipe is presented, that generates more consistent machines with similar on-average performance, but less extra-sample loss variance and less feature bias. A use case is provided by applying the recipe onto the atrial flutter localization problem.
