Evaluating the Features of Indoor Positioning Systems Using Explainable AI

This paper investigates the application of Explainable AI (XAI) techniques in evaluating the features of indoor positioning systems, with a focus on improving model transparency and interpretability. Indoor positioning systems, crucial for location-based services in complex environments, rely heavily on machine learning models that often operate as black boxes. By performing SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations), this study aims to determine the most influential features by driving model predictions and provide a deeper understanding of their roles within the system. In this study, we investigate the features of RSSI values to check their importance and interactions, enhance the interpretability of the model through SHAP and LIME analyses, and guide future improvements in system accuracy and reliability.