论文标题
对机器学习算法的降低方法的实验研究，并应用了精神计量学
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics
论文作者
论文摘要
开发可解释的机器学习模型已成为越来越重要的问题。数据科学家能够开发可解释模型的一种方法是使用缩小技术。在本文中，我们研究了几种缩小维度技术，包括在网络心理计量学文献中开发的两种称为探索性图分析（EGA）和唯一变量分析（UVA）中的方法。我们将EGA和UVA与机器学习文献中常见的其他两种缩小技术（主要组件分析和独立组件分析）进行了比较，并且对变量实际数据没有减少。我们表明，EGA和UVA的性能和其他还原技术或没有降低。与以前的文献一致，我们表明降低可以减少，增加或提供与变量降低相同的准确性。我们的初步结果发现，降低尺寸倾向于在用于分类任务时会带来更好的性能。
Developing interpretable machine learning models has become an increasingly important issue. One way in which data scientists have been able to develop interpretable models has been to use dimension reduction techniques. In this paper, we examine several dimension reduction techniques including two recent approaches developed in the network psychometrics literature called exploratory graph analysis (EGA) and unique variable analysis (UVA). We compared EGA and UVA with two other dimension reduction techniques common in the machine learning literature (principal component analysis and independent component analysis) as well as no reduction to the variables real data. We show that EGA and UVA perform as well as the other reduction techniques or no reduction. Consistent with previous literature, we show that dimension reduction can decrease, increase, or provide the same accuracy as no reduction of variables. Our tentative results find that dimension reduction tends to lead to better performance when used for classification tasks.
