Explainable Predictive Modeling for Limited Spectral Data. (arXiv:2202.04527v1 [cs.LG])

Feature selection of high-dimensional labeled data with limited observations
is critical for making powerful predictive modeling accessible, scalable, and
interpretable for domain experts. Spectroscopy data, which records the
interaction between matter and electromagnetic radiation, particularly holds a
lot of information in a single sample. Since acquiring such high-dimensional
data is a complex task, it is crucial to exploit the best analytical tools to
extract necessary information. In this paper, we investigate the most commonly
used feature selection techniques and introduce …

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