The importance of parameter selection in supervised learning is well known.
However, due to the many parameter combinations, an incomplete or an
insufficient procedure is often applied. This situation may cause misleading or
confusing conclusions. In this opinion paper, through an intriguing example we
point out that the seriousness goes beyond what is generally recognized. In the
topic of multi-label classification for medical code prediction, one
influential paper conducted a proper parameter selection on a set, but when
moving to a subset of frequently occurring labels, the authors used the same
parameters without a separate tuning. The set of frequent labels became a
popular benchmark in subsequent studies, which kept pushing the state of the
art. However, we discovered that most of the results in these studies cannot
surpass the approach in the original paper if a parameter tuning had been
conducted at the time. Thus it is unclear how much progress the subsequent
developments have actually brought. The lesson clearly indicates that without
enough attention on parameter selection, the research progress in our field can
be uncertain or even illusive.

本文强调了参数选择在监督学习中的重要性，并通过一个例子指出，没有足够的关注和调整参数选择，我们领域的研究进展可能是不确定或甚至是虚幻的。在医学编码预测的多标签分类主题中，一个重要的研究在一个参数集上进行了适当的参数选择，但是当移动到常见标签的子集时，作者使用了相同的参数而没有进行单独的调整。该常见标签集成为后续研究的热门基准，但在大多数这些研究中，如果在此期间进行了参数调整，则无法超越原始论文的方法。

参数选择：为什么我们应该更加关注它

Parameter Selection: Why We Should Pay More Attention to It

Given the clinical notes written in electronic health records (EHRs), it is
challenging to predict the diagnostic codes which is formulated as a
multi-label classification task. The large set of labels, the hierarchical
dependency, and the imbalanced data make this prediction task extremely hard.
Most existing work built a binary prediction for each label independently,
ignoring the dependencies between labels. To address this problem, we propose a
two-stage framework to improve automatic ICD coding by capturing the label
correlation. Specifically, we train a label set distribution estimator to
rescore the probability of each label set candidate generated by a base
predictor. This paper is the first attempt at learning the label set
distribution as a reranking module for medical code prediction. In the
experiments, our proposed framework is able to improve upon best-performing
predictors on the benchmark MIMIC datasets. The source code of this project is
available at this https URL

提出一个两阶段的框架，通过捕捉标签相关性来提高自动 ICD 编码，利用标签集分布估计器和基准预测器来生成每个标签集候选项的概率，并重排序，以解决测量标签相关性的问题。