Customer satisfaction prediction with Michigan-style learning classifier system

Keivan Borna, Shokoofeh Hoseini, Mohammad Ali Mehdi Aghaei
2019 SN Applied Sciences  
Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied
more » ... ustomers. Three different Rule Compaction strategies are used to compare the rule population's accuracy and micro/macro population size. The result specifies features that mostly influence prediction. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
doi:10.1007/s42452-019-1493-1 fatcat:4hpv3yadk5c3tpm25tihmetncq