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Revealing factors influencing polymer degradation with rank-based machine learning.

ABSTRACT: The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis. In this study, we have developed a platform for evaluating the degradability of polymers that is suitable for such data, using a rank-based machine learning technique based on RankSVM. We then made a ranking model to evaluate the degradability of polymers, integrating three datasets on the degradability of polymers that are measured by different means and conditions. Analysis of this ranking model with a decision tree revealed factors that dominate the degradability of polymers.

SUBMITTER: Yuan W

PROVIDER: S-EPMC10724228 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Revealing factors influencing polymer degradation with rank-based machine learning.

Yuan Weilin W Hibi Yusuke Y Tamura Ryo R Sumita Masato M Nakamura Yasuyuki Y Naito Masanobu M Tsuda Koji K

Patterns (New York, N.Y.) 20230925 12

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis. In this study, we have developed a platform for evaluating the degradability of polymers that is suitable for such data, using a ...[more]

PMID: 38106610

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Dataset Information

Revealing factors influencing polymer degradation with rank-based machine learning.

Publications

Revealing factors influencing polymer degradation with rank-based machine learning.

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