Explainable predictive modeling for limited spectral data

Frantishek Akulich

doi:10.1016/j.chemolab.2022.104572

2022-06-01

DOI：10.1016/j.chemolab.2022.104572

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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 highdimensional 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. Furthermore, we consider a wide range of machine learning models that have been proven to be successful for the prediction of the Cetane Number of fuels. 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. The evaluation is performed for both the full model and reduced models using different feature selection techniques on a real dataset. Finally, we propose a correctness metric for the feature selection techniques to assess the conformance of the selected subset of features to the domain expertise. As a result, the Support Vector Regression yields better prediction accuracy and generalization power as it leads to less complex and computationally more efficient than model Neural Network. More importantly, using the reduced subset of features from original data creates a pathway to deploying less complex, scalable, and explainable prediction models.

Keyword:

Interpretability

Predictive modeling

Explainable AI

Spectral data

Feature selection

Interpretability

Predictive modeling

Explainable AI

Spectral data

Feature selection