Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification

Gabriel Marín Díaz (), Raquel Gómez Medina and José Alberto Aijón Jiménez
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Gabriel Marín Díaz: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
Raquel Gómez Medina: Science and Aerospace Department, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain
José Alberto Aijón Jiménez: Science and Aerospace Department, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain

Mathematics, 2024, vol. 12, issue 18, 1-27

Abstract: The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data.

Keywords: Fuzzy C-Means; explainable AI; XAI; SHAP values; LIME; citizen science; astronomy; machine learning (ML) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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