1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1
Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employ…
stat.ML2020★ 1 cited
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
Everton Jose Santana, Felipe Rodrigues dos Santos, Saulo Martiello Mastelini +2
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy…