学习迁移
机器学习
数量结构-活动关系
化学
反应性(心理学)
人工智能
计算机科学
知识转移
传输(计算)
样品(材料)
训练集
分子描述符
数据挖掘
色谱法
病理
医学
并行计算
知识管理
替代医学
作者
Shifa Zhong,Yanping Zhang,Huichun Zhang
标识
DOI:10.1021/acs.est.1c04883
摘要
To develop predictive models for the reactivity of organic contaminants toward four oxidants─SO4•-, HClO, O3, and ClO2─all with small sample sizes, we proposed two approaches: combining small data sets and transferring knowledge between them. We first merged these data sets and developed a unified model using machine learning (ML), which showed better predictive performance than the individual models for HClO (RMSEtest: 2.1 to 2.04), O3 (2.06 to 1.94), ClO2 (1.77 to 1.49), and SO4•- (0.75 to 0.70) because the model "corrected" the wrongly learned effects of several atom groups. We further developed knowledge transfer models for three pairs of the data sets and observed different predictive performances: improved for O3 (RMSEtest: 2.06 to 2.01)/HClO (2.10 to 1.98), mixed for O3 (2.06 to 2.01)/ClO2 (1.77 to 1.95), and unchanged for ClO2 (1.77 to 1.77)/HClO (2.1 to 2.1). The effectiveness of the latter approach depended on whether there was consistent knowledge shared between the data sets and on the performance of the individual models. We also compared our approaches with multitask learning and image-based transfer learning and found that our approaches consistently improved the predictive performance for all data sets while the other two did not. This study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve ML model performance.
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