面向监督学习的潜在变量建模。

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2023-11-01 Epub Date: 2023-05-25 DOI:10.1080/00273171.2023.2182753
Booil Jo, Trevor J Hastie, Zetan Li, Eric A Youngstrom, Robert L Findling, Sarah McCue Horwitz
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引用次数: 0

摘要

尽管有潜在的好处,但使用基于潜变量(LV)建模生成的预测目标在监督学习中并不常见,而监督学习是开发预测模型的主要框架。在监督学习中,通常假设要预测的结果是清晰且容易获得的,因此在预测结果之前验证结果是一个陌生的概念,也是一个不必要的步骤。LV建模的通常目标是推理,因此在监督学习和预测上下文中使用它需要一个重大的概念转变。本研究提出了将LV模型整合到监督学习中所必需的方法调整和概念转变。研究表明,通过结合传统的LV建模、心理测量学和监督学习,这种整合是可能的。在这个跨学科的学习框架中,使用LV建模和基于临床验证器系统地验证产生实际结果是两个主要策略。在使用躁狂症状纵向评估(LAMS)研究数据的示例中,通过灵活的LV建模生成了大量候选结果。这表明,这种探索性的情况可以作为一个机会,利用当代科学和临床见解来定制理想的预测目标。
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Reorienting Latent Variable Modeling for Supervised Learning.

Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV modeling is inference, and therefore using it in supervised learning and in the prediction context requires a major conceptual shift. This study lays out methodological adjustments and conceptual shifts necessary for integrating LV modeling into supervised learning. It is shown that such integration is possible by combining the traditions of LV modeling, psychometrics, and supervised learning. In this interdisciplinary learning framework, generating practical outcomes using LV modeling and systematically validating them based on clinical validators are the two main strategies. In the example using the data from the Longitudinal Assessment of Manic Symptoms (LAMS) Study, a large pool of candidate outcomes is generated by flexible LV modeling. It is demonstrated that this exploratory situation can be used as an opportunity to tailor desirable prediction targets taking advantage of contemporary science and clinical insights.

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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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