Abstract Explainable artificial intelligence provides a framework to ensure fairness, reduce bias, and improve machine learning models. However, explainability should not only appear in the model’s outputs but also assist experts in understanding the entire learning process. It enables easier model verification and helps identify relationships learned during training. This study introduces a feature selection approach based on a novel integration of association rule mining and meta-learning, designed to enhance both transparency and performance. We conducted a case study using the UNSW-NB15 dataset combining traditional feature selection techniques with association rule mining to identify additional interpretable features, yielding high predictive performance. The resulting minimal feature subset consists of four features achieving better performance compared to previous studies. Results demonstrate the potential of combining supervised, unsupervised, and interpretable methods to build an explainable meta-learning layer for feature recommendation, contributing to efficient, scalable, and transparent intelligent systems aligned with modern high-performance computing paradigms.
Mallo-Fernández et al. (Tue,) studied this question.