对化学中不平衡数据挑战的机器学习方法的审查
Jian Jiang1,2, Chunhuan Zhang1, Lu Ke1
1Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University Wuhan 430200 P R. China jjiang@wtu.edu.cn.
Chemical science
|April 24, 2025
概括
化学中的不平衡数据阻碍了机器学习 (ML) 模型的准确性. 本综述涵盖了ML技术,如重新抽样和数据增强,以改善对代表性不足的化学数据的预测.
科学领域:
- 化学 化学 化学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 不平衡的数据集是化学中常见的挑战,导致有偏见的机器学习 (ML) 和深度学习 (DL) 模型.
- 这种偏差损害了对代表性不足的化学类预测的准确性和可靠性,限制了模型的适用性.
研究的目的:
- 为解决化学不平衡数据的突出ML方法提供全面的审查.
- 在各种化学应用中评估这些技术,如药物发现,材料科学,化学信息学和催化.
主要方法:
- 复习著名的ML方法,包括重新采样,数据增强,算法策略和功能工程.
- 在化学应用的背景下对方法的评估.
- 探索未来的方向,例如通过物理模型,大型语言模型 (LLM) 和高级数学来增强数据.
主要成果:
- 识别和分类关键的ML技术来处理不平衡的化学数据.
- 评估了这些方法在各种化学子领域的适用性和局限性.
- 强调了平衡数据对材料设计和生产的好处.
结论:
- 机器学习技术为化学中的不平衡数据问题提供了可行的解决方案.
- 未来的研究应该专注于先进的数据增强策略和利用LLM来提高模型性能.
- 实现数据平衡对于推进新材料设计和生产至关重要.
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