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== Terminology ==
The name, "Learning Classifier System (LCS)", is a bit misleading since there are many [[machine learning]] algorithms that 'learn to classify' (e.g. [[decision tree]]s, [[decision stream]]s <ref name="Decision stream">{{cite journal|author1=Ignatov, D.Yu.|author2=Ignatov, A.D.|title=Decision Stream: Cultivating Deep Decision Trees|date=2017|arxiv=1704.07657|url=https://arxiv.org/pdf/1704.07657.pdf}}</ref>, [[artificial neural network]]s), but are not LCSs. The term 'rule-based machine learning (RBML)' is useful, as it more clearly captures the essential 'rule-based' component of these systems, but it also generalizes to methods that are not considered to be LCSs (e.g. [[association rule learning]], or [[artificial immune system]]s). More general terms such as, 'genetics-based machine learning', and even 'genetic algorithm'<ref name=":8">Congdon, Clare Bates. "A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research." PhD diss., The University of Michigan, 1995.</ref> have also been applied to refer to what would be more characteristically defined as a learning classifier system. Due to their similarity to [[genetic algorithm]]s, Pittsburgh-style learning classifier systems are sometimes generically referred to as 'genetic algorithms'. Beyond this, some LCS algorithms, or closely related methods, have been referred to as 'cognitive systems',<ref name=":2" /> 'adaptive agents', '[[production system (computer science)|production system]]s', or generically as a 'classifier system'.<ref>{{Cite journal|last=Booker|first=L. B.|last2=Goldberg|first2=D. E.|last3=Holland|first3=J. H.|date=1989-09-01|title=Classifier systems and genetic algorithms|url=http://www.sciencedirect.com/science/article/pii/0004370289900507|journal=Artificial Intelligence|volume=40|issue=1|pages=235–282|doi=10.1016/0004-3702(89)90050-7}}</ref><ref>Wilson, Stewart W., and David E. Goldberg. "A critical review of classifier systems." In ''Proceedings of the third international conference on Genetic algorithms'', pp. 244-255. Morgan Kaufmann Publishers Inc., 1989.</ref> This variation in terminology contributes to some confusion in the field.
Up until the 2000's nearly all learning classifier system methods were developed with reinforcement learning problems in mind. As a result, the term ‘learning classifier system’ was commonly defined as the combination of ‘trial-and-error’ reinforcement learning with the global search of a genetic algorithm. Interest in supervised learning applications, and even unsupervised learning have since broadened the use and definition of this term.
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