Optimised Hybrid-Model Selection for the Autonomous Relevance Technique Explainable Framework

Despite evidence indicating that black box models are problematic and not fully utilised across various domains, researchers persist in developing and employing these techniques without offering insights into their decision-making processes. As a result, in critical areas such as the medical domain, a lack of trust and scepticism persists. This paper introduces the OH-ART-Explain (Optimized Hybrid Model Selection for Autonomous Relevance Technique Explainable) framework as an enhanced version of the ART-Explain framework. The OH-ART-Explain framework utilises an optimisation algorithm called 'Optimized Hybrid Model Selection' to identify the best deep learning model, feature extraction layer, and rule-based classification combination, leading to more robust results. Unlike the original ART-Explain framework, OH-ART-Explain does not limit itself to a single potential deep learning classifier, layer, or rule-based classification combination, thus improving flexibility and performance. Additionally, we propose the Reduced Graphical Explanation Technique (R-GET) to simplify the visualisation and interpretation of the rule-based classification process within the OH-ART-Explain framework. We evaluated the proposed framework using eight deep learning classifiers, two rule-based classifiers, and three medical datasets. Our experimentation result's key findings demonstrate that the synergy between the base model and rule-based classifier is interdependent. The combination within the OH-ART-Explain framework produces higher results than using only the deep learning classifier.