Customer satisfaction prediction with Michigan-style learning classifier system
Keyword(s):
Abstract 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.
2017 ◽
Vol 21
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pp. 856-867
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2013 ◽
Vol 347-350
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pp. 3208-3211
2020 ◽
Vol 8
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pp. 1020-1026
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2013 ◽
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pp. 416-420
2011 ◽
Vol 137
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pp. 12-16
2010 ◽
Vol 19
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pp. 275-296
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