Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer.
Environ Sci Technol
; 56(1): 681-692, 2022 01 04.
Article
en En
| MEDLINE
| ID: mdl-34908403
ABSTRACT
To develop predictive models for the reactivity of organic contaminants toward four oxidantsâSO4â¢-, HClO, O3, and ClO2âall 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 SO4â¢- (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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Ozono
/
Oxidantes
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Environ Sci Technol
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos