Towards ML Explainability with Rough Sets, Clustering, and Dimensionality Reduction

This study discusses some essential problems of explainable machine learning applications in the FMCG market. The solution combines several machine learning techniques, including clustering, dimensionality reduction, rough set reducts, and rule-based explanations. We propose a novel approach to improve human-computer interaction with the XAI prototype method by generating human-readable cluster descriptions, emphasizing each cluster's most discernible characteristics. To evaluate our method, we refer to the challenging task of demand prediction. The results confirmed that we could achieve five times better work performance without losing quality.