Customer satisfaction prediction with Michigan-style learning classifier system

Customer satisfaction prediction with Michigan-style learning classifier system

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...

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Bibliographic Details
Published in:SN applied sciences Vol. 1; no. 11; p. 1450
Main Authors: Borna, Keivan, Hoseini, Shokoofeh, Aghaei, Mohammad Ali Mehdi
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-11-2019
Springer Nature B.V
Subjects:
Accuracy
Adaptive systems
Algorithms
Applied and Technical Physics
Artificial intelligence
Big Data and Applied Deep Learning: From Science to Applications
Case Study
Chemistry/Food Science
Classification
Classifiers
Customer satisfaction
Datasets
Earth Sciences
Engineering
Engineering: Data Science
Environment
Feedback
Learning algorithms
Machine learning
Materials Science
Population number
Learning classifier system
Michigan style LCS
Evolutionary algorithm
Machine learning
Prediction
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Summary: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 customers. 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.
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-019-1493-1