Interpreting support vector machines applied in laser-induced breakdown spectroscopy

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Authors

KEPES Erik VRABEL Jakub ADAMOVSKÝ Ondřej STRITEZSKA Sara MODLITBOVA Pavlina PORIZKA Pavel KAISER Jozef

Year of publication 2022
Type Article in Periodical
Magazine / Source Analytica Chimica Acta
MU Faculty or unit

Faculty of Science

Citation
Web https://www.sciencedirect.com/science/article/pii/S0003267021011788?via%3Dihub
Doi http://dx.doi.org/10.1016/j.aca.2021.339352
Keywords LIBS; Classification; Feature importance; SVM; Interpretable machine learning
Description Laser-induced breakdown spectroscopy is often combined with a multivariate black box model-such as support vector machines (SVMs)-to obtain desirable quantitative or qualitative results. This approach carries obvious risks when practiced in high-stakes applications. Moreover, the lack of understanding of a black-box model limits the user's ability to fine-tune the model. Thus, here we present four approaches to interpret SVMs through investigating which features the models consider important in the classification task of 19 algal and cyanobacterial species. The four feature importance metrics are compared with popular approaches to feature selection for optimal SVM performance. We report that the distinct feature importance metrics yield complementary and often comparable information. In addition, we identify our SVM model's bias towards features with a large variance, even though these features exhibit a significant overlap between classes. We also show that the linear and radial basis kernel SVMs weight the same features to the same degree.
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