Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification
This addresses the need for explainable models in customer churn prediction for telecom businesses, but it is incremental as it builds on existing methods like fuzzy-set theory and HUIM.
The study tackled customer churn prediction in telecommunications by developing Highly Associated Fuzzy Churn Patterns (HAFCP) using machine learning and fuzzy-set theory to enhance explainability, with experiments on five datasets showing mixed performance results including some notable improvements.
Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasizes not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-\textit{k} HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models.