An Agnostic Interpretable Machine Learning for Knowledge Discovery Framework

This paper introduces our Agnostic Interpretable Machine Learning for Knowledge Discovery (AIM4KD) framework. Our approach to emotional stress detection is innovative, as it integrates dataset preprocessing, classification, knowledge discovery techniques, and visualisation methods to augment traditional machine learning methodologies. The key innovation is our departure from conventional machine learning strategies, adopting an agnostic approach that involves preprocessing multiple dataset versions and employing multiple classification algorithms with dynamic configuration settings. This strategy has yielded high-quality logic rules, ensuring that the most significant patterns surface for effective knowledge discovery. We validated the framework through extensive experiments using the DEAP dataset, a crucial resource in Affective Computing for examining physiological changes during emotional episodes. When benchmarked against traditional preprocessing and classification methods using tree and forest algorithms, AIM4KD produced, on average, an impressive 22-fold increase in high-quality rules. These experiments highlight the framework’s effectiveness in accurately detecting emotional stress states. Furthermore, the findings demonstrate AIM4KD’s capacity to handle complex multimodal datasets and underscore the critical role of interpretability in machine learning. This research emphasises AIM4KD’s potential to improve decision-making processes in physiological data analysis and its applicability across various domains featuring intricate time-series data. For instance, experts in health or finance can utilise AIM4KD to understand the underlying time series dynamics. To encourage broader adoption and facilitate future research, we will release the AIM4KD framework code and the DEAP dataset preprocessing script publicly at https://github.com/CyberNaut-AU/AIM4KD upon the paper’s final acceptance.