Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques

IF 3.6 2区管理学 Q2 BUSINESS

Journal of Management Analytics Pub Date : 2024-08-12 DOI:10.1080/23270012.2024.2377168

Shengkun Xie, Jin Zhang

引用次数: 0

Abstract

Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine...

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利用机器学习技术处理高度不平衡数据，对汽车碰撞死亡率进行分类

准确预测车祸中的致命事件对健康管理具有重要意义。本文探讨了不平衡数据处理技术在机器学习中的应用。

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

Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

13.30

自引率

3.40%

发文量

期刊介绍： The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.

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