距离相关确定独立筛选用于帕金森病嗓音数据的加速特征选择
Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson's Disease Vocal Data
摘要：
随着大量的机器学习方法的出现，以及将它们全部用于集成方法的诱惑，拥有一种与模型无关的特征选择方法是非常诱人的。主成分分析是在1901年开发的，从那以后一直是这个角色的有力竞争者，但最终是一种无监督的方法。它不能保证选择的特征具有良好的预测能力，因为它不知道预测的是什么。为此，Peng等人在2005年开发了最小冗余-最大相关性（mRMR）方法。它不仅使用预测器之间的互信息，而且在其计算中还包括与响应的互信息。估计互信息和熵往往是昂贵的和有问题的努力，这导致即使对于在留一个对象重叠情况下大约750乘750的数据集也有过多的处理时间。为了解决这个问题，我们使用了2012年的一种称为距离相关性确定独立筛选（DC-SIS）的方法，该方法使用Székely等人的距离相关性度量来选择与响应具有最大依赖性的特征。我们表明，这种方法产生统计上无法区分的结果，帕金森氏病声乐诊断数据的mRMR选择方法快90倍。
英文摘要：
With the abundance of machine learning methods available and the temptation of using them all in an ensemble method, having a model-agnostic method of feature selection is incredibly alluring. Principal component analysis was developed in 1901 and has been a strong contender in this role since, but in the end is an unsupervised method. It offers no guarantee that the features that are selected have good predictive power because it does not know what is being predicted. To this end, Peng et al. developed the minimum redundancy-maximum relevance (mRMR) method in 2005. It uses the mutual information not only between predictors but also includes the mutual information with the response in its calculation. Estimating mutual information and entropy tend to be expensive and problematic endeavors, which leads to excessive processing times even for dataset that is approximately 750 by 750 in a Leave-One-Subject-Out jackknife situation. To remedy this, we use a method from 2012 called Distance Correlation Sure Independence Screening (DC-SIS) which uses the distance correlation measure of Székely et al. to select features that have the greatest dependence with the response. We show that this method produces statistically indistinguishable results to the mRMR selection method on Parkinson's Disease vocal diagnosis data 90 times faster.
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