Counterfactual-Based Feature Importance for Explainable Regression of Manufacturing Production Quality Measure

Antonio L. Alfeo, Antonio L. Alfeo, Mario Cimino, Mario Cimino

2024

Abstract

Machine learning (ML) methods need to explain their reasoning to allow professionals to validate and trust their predictions, and employ those in real-world decision-making processes. To do so, explainable artificial intelligence (XAI) methods based on feature importance can be employed, even though those can be very computationally expensive. Moreover, it can be challenging to determine whether an XAI technique might introduce bias into the explanation (e.g., overestimating or underestimating the feature importance) in the absence of some reference feature importance measure or even some domain knowledge from which deriving an expected importance level for each feature. We address both these issues by (i) employing a counterfactual-based strategy, i.e. deriving a measure of feature importance by checking if some minor changes in one feature’s values significantly affect the ML model’s regression outcome, and (ii) employing both synthetic and real-world industrial data coupled with the expected degree of importance for each feature. Our experimental results show that the proposed approach (BoCSoRr) is more reliable and way less computationally expensive than DiCE, a well-known counterfactual-based XAI approach able to provide a measure of feature importance.

Paper Citation

in Harvard Style

L. Alfeo A. and Cimino M. (2024). Counterfactual-Based Feature Importance for Explainable Regression of Manufacturing Production Quality Measure. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 48-56. DOI: 10.5220/0012369600003654

in Bibtex Style

@conference{icpram24,
author={Antonio L. Alfeo and Mario Cimino},
title={Counterfactual-Based Feature Importance for Explainable Regression of Manufacturing Production Quality Measure},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={48-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012369600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Counterfactual-Based Feature Importance for Explainable Regression of Manufacturing Production Quality Measure
SN - 978-989-758-684-2
AU - L. Alfeo A.
AU - Cimino M.
PY - 2024
SP - 48
EP - 56
DO - 10.5220/0012369600003654
PB - SciTePress