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Reinforcement Feature Transformation for Polymer Property Performance Prediction by Hu, Xuanming; Wang, Dongjie; Ying, Wangyang; Fu, Yanjie is a scholarly article available to read on EtoBox.

What is Reinforcement Feature Transformation for Polymer Property Performance Prediction about?

Polymer property performance prediction aims to forecast specific features or attributes of polymers, which has become an efficient approach to measuring their performance. However, existing machine learning models face challenges in effectively learning polymer representations due to low-quality polymer datasets, which consequently impact their overall performance. This study focuses on improving polymer property performance prediction tasks by reconstructing an optimal and explainable descriptor representation space. Nevertheless, prior research such as feature engineering and representation learning can only partially solve this task since they are either labor-incentive or unexplainable. This raises two issues: 1) automatic transformation and 2) explainable enhancement. To tackle these issues, we propose our unique Traceable Group-wise Reinforcement Generation Perspective. Specifically, we redefine the reconstruction of the representation space into an interactive process, combining nested generation and selection. Generation creates meaningful descriptors, and selection eliminates redundancies to control descriptor sizes. Our approach employs cascading reinforcement learning w

Author

Hu, Xuanming; Wang, Dongjie; Ying, Wangyang; Fu, Yanjie

Published

2024

Language

EN