Explainable Predictive Modeling for Limited Spectral Data

TL;DR

This paper explores feature selection and explainable AI techniques for high-dimensional spectral data with limited observations, aiming to improve interpretability, optimize data collection, and facilitate real-world deployment of spectroscopic sensors.

Contribution

It introduces the application of explainable AI methods to interpret predictions on limited spectral data and evaluates robustness across different scenarios.

Findings

Explainability enhances model transparency for domain experts.

Feature selection improves prediction accuracy with limited data.

Robust evaluation scenarios reveal noise effects on model performance.

Abstract

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 applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge. Due to the instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process through the miniaturization of the spectrometer device. Reducing the device size and power and therefore cost is a requirement for the real-world deployment of such a sensor-to-prediction system as a whole. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice of the developed methodologies and to uncover the hidden effect of noise sources on the final outcome.