Abstract
Given that churn management is a crucial endeavour for firms aiming to retain valuable customers, the capacity to forecast customer churn is indispensable. We use rough and fuzzy set based classifier to predict customer churn on the example of the Bank Customer Churn dataset. Rough set theory offers techniques for handling incomplete or missing data. By utilizing lower and upper approximation concepts, the system can still perform prediction even when certain feature values are missing, what we show in the paper for every combination of missing features. Moreover, we determine feature importance coefficient evaluated through two different means: directly from data and from the working classifier. Rough set-based systems can be integrated with other machine learning and data mining techniques, and we use the LEM-2 rule induction algorithm to create a rule base for the rough-fuzzy classifier.
Paper Type
Full Paper
DOI
10.62036/ISD.2024.32
Customer Churn Prediction by Rough Neuro-Fuzzy Classifier with CA Defuzzification
Given that churn management is a crucial endeavour for firms aiming to retain valuable customers, the capacity to forecast customer churn is indispensable. We use rough and fuzzy set based classifier to predict customer churn on the example of the Bank Customer Churn dataset. Rough set theory offers techniques for handling incomplete or missing data. By utilizing lower and upper approximation concepts, the system can still perform prediction even when certain feature values are missing, what we show in the paper for every combination of missing features. Moreover, we determine feature importance coefficient evaluated through two different means: directly from data and from the working classifier. Rough set-based systems can be integrated with other machine learning and data mining techniques, and we use the LEM-2 rule induction algorithm to create a rule base for the rough-fuzzy classifier.
Recommended Citation
Scherer, M. & Nowicki, R. (2024). Customer Churn Prediction by Rough Neuro-Fuzzy Classifier with CA Defuzzification. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.32