A flexible model-free prediction-based framework for feature ranking.
A flexible model-free prediction-based framework for feature ranking.
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发表时间：
2021-05
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通讯作者：
Tong X
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作者：
Li JJ;Chen YE;Tong X
尽管有许多统计和机器学习工具可用于联合特征建模，但许多科学家对特征进行了边缘研究，即一次研究一个特征。这部分是由于训练和惯例，但也源于科学家对简单可视化和可解释性的强烈兴趣。因此，在科学发现的过程中，对某些预测任务（如癌症驱动基因的预测）的边缘特征排序被广泛应用。在这项工作中，我们专注于二元分类的边缘排序，这是最常见的预测任务之一。我们认为，最广泛使用的边际排序标准，包括Pearson相关性、两样本t检验和两样本Wilcoxon秩和检验，没有充分考虑特征分布和预测目标。为了解决实践中的这一差距，我们提出了两个与两个预测目标相对应的排名标准：经典标准（CC）和Neyman-Pearson标准（NPC），两者都使用无模型非参数实现来适应不同的特征分布。从理论上讲，我们证明了在规则条件下，这两个标准都以高概率实现了与其总体水平对应的样本水平排名一致。此外，当样本中的两个类别比例偏离总体时，NPC对抽样偏差具有鲁棒性。这一特性使NPC在抽样偏差普遍存在的生物医学研究中具有良好的潜力。我们展示了CC和NPC在仿真和实际数据研究中的使用及其相对优势。我们的无模型的基于目标的排序思想可以扩展到对特征子集进行排序，并且可以推广到其他预测任务和学习目标。
Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time. This is partly due to training and convention but also roots in scientists’ strong interests in simple visualization and interpretability. As such, marginal feature ranking for some predictive tasks, e.g., prediction of cancer driver genes, is widely practiced in the process of scientific discoveries. In this work, we focus on marginal ranking for binary classification, one of the most common predictive tasks. We argue that the most widely used marginal ranking criteria, including the Pearson correlation, the two-sample t test, and two-sample Wilcoxon rank-sum test, do not fully take feature distributions and prediction objectives into account. To address this gap in practice, we propose two ranking criteria corresponding to two prediction objectives: the classical criterion (CC) and the Neyman-Pearson criterion (NPC), both of which use model-free nonparametric implementations to accommodate diverse feature distributions. Theoretically, we show that under regularity conditions, both criteria achieve sample-level ranking that is consistent with their population-level counterpart with high probability. Moreover, NPC is robust to sampling bias when the two class proportions in a sample deviate from those in the population. This property endows NPC good potential in biomedical research where sampling biases are ubiquitous. We demonstrate the use and relative advantages of CC and NPC in simulation and real data studies. Our model-free objective-based ranking idea is extendable to ranking feature subsets and generalizable to other prediction tasks and learning objectives.