Improving Predictive Classification Models Using Generative Adversarial Networks in the Prediction of Suicide Attempts

IF 2 4区心理学 Q2 Psychology

Measurement and Evaluation in Counseling and Development Pub Date : 2021-04-23 DOI:10.1080/07481756.2021.1906156

Anthony A. Mangino, Kendall A. Smith, W. H. Finch, M. Hernández-Finch

引用次数: 0

Abstract

Abstract A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network.

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利用生成对抗性网络改进自杀未遂预测分类模型

摘要许多机器学习方法可以用于自杀未遂的预测。然而，在数据不平衡的情况下，许多模型并不能很好地预测新病例。本研究通过使用生成对抗性网络改进了对自杀企图的预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Measurement and Evaluation in Counseling and Development Multiple-

CiteScore

2.40

自引率

10.00%

发文量

期刊介绍： Measurement and Evaluation in Counseling and Development is an official journal of the Association of Assessment and Research in Counseling (AARC), a member association and division of the American Counseling Association. Articles range in appeal from those that deal with theoretical and other problems of the measurement specialist to those directed to the administrator, the counselor, or the personnel worker--in schools and colleges, public and private agencies, business, industry, and government. All articles clearly describe implications for the counseling field and for practitioners, educators, administrators, researchers, or students in assessment, measurement, and evaluation.