当前位置: X-MOL 学术Psychological Methods › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology.
Psychological Methods ( IF 10.929 ) Pub Date : 2023-08-10 , DOI: 10.1037/met0000586
Philipp Sterner 1 , David Goretzko 1 , Florian Pargent 1
Affiliation  

Psychology has seen an increase in the use of machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false positive or false negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive machine learning (CSL) methods can take different cost ratios into account. We present the mathematical foundations and introduce a taxonomy of the most commonly used CSL methods, before demonstrating their application and usefulness on psychological data, that is, the drug consumption data set (N = 1, 885) from the University of California Irvine ML Repository. In our example, all demonstrated CSL methods noticeably reduced mean misclassification costs compared to regular ML algorithms. We discuss the necessity for researchers to perform small benchmarks of CSL methods for their own practical application. Thus, our open materials provide R code, demonstrating how CSL methods can be applied within the mlr3 framework (https://osf.io/cvks7/). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译:

一切皆有代价:成本敏感型机器学习的基础及其在心理学中的应用。

心理学中机器学习 (ML) 方法的使用有所增加。在许多应用中,观察结果被分为两组之一(二元分类)。现成的分类算法假设错误分类(误报或漏报)的成本是相等的。因为这通常是不合理的(例如,在临床心理学中),成本敏感的机器学习(CSL)方法可以考虑不同的成本比率。我们介绍了数学基础并介绍了最常用的 CSL 方法的分类,然后展示了它们在心理数据(即来自加州大学欧文分校 ML 存储库的药物消耗数据集 (N = 1, 885))上的应用和有用性。在我们的示例中,与常规 ML 算法相比,所有演示的 CSL 方法都显着降低了平均错误分类成本。我们讨论了研究人员为自己的实际应用执行 CSL 方法的小型基准测试的必要性。因此,我们的开放材料提供了 R 代码,演示了如何在 mlr3 框架内应用 CSL 方法 (https://osf.io/cvks7/)。(PsycInfo 数据库记录 (c) 2023 APA,保留所有权利)。
更新日期:2023-08-10
down
wechat
bug