The Application of a Random Forest Classifier to ToF-SIMS Imaging Data DOI Creative Commons
Mariya A. Shamraeva, Theodoros Visvikis,

Stefanos Zoidis

et al.

Published: Aug. 1, 2024

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information of surfaces at the microscale. However, hyperspectral nature ToF-SIMS datasets constitutes can be challenging to analyze and interpret. Both supervised unsupervised Machine Learning (ML) approaches are increasingly useful help data. Random Forest (RF) has emerged as robust powerful algorithm for processing mass spectrometry This machine learning approach offers several advantages, including accommodating non-linear relationships, robustness outliers in data, managing high-dimensional feature space, mitigating risk overfitting. The application RF facilitates classification complex compositions identification features contributing these classifications. tutorial aims assist non-experts either or apply datasets.

Language: Английский

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data DOI Creative Commons
Mariya A. Shamraeva, Theodoros Visvikis,

Stefanos Zoidis

et al.

Published: Aug. 1, 2024

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information of surfaces at the microscale. However, hyperspectral nature ToF-SIMS datasets constitutes can be challenging to analyze and interpret. Both supervised unsupervised Machine Learning (ML) approaches are increasingly useful help data. Random Forest (RF) has emerged as robust powerful algorithm for processing mass spectrometry This machine learning approach offers several advantages, including accommodating non-linear relationships, robustness outliers in data, managing high-dimensional feature space, mitigating risk overfitting. The application RF facilitates classification complex compositions identification features contributing these classifications. tutorial aims assist non-experts either or apply datasets.

Language: Английский

Citations

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