Learning classifier system: Difference between revisions

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[[File:Function approximation with LCS rules.jpg|thumb|2D visualization of LCS rules learning to approximate a 3D function. Each blue ellipse represents an individual rule covering part of the solution space. (Adapted from images taken from XCSF<ref name=":9">{{Cite journal|last=Stalph|first=Patrick O.|last2=Butz|first2=Martin V.|date=2010-02-01|title=JavaXCSF: The XCSF Learning Classifier System in Java|journal=SIGEVOlution|volume=4|issue=3|pages=16–19|doi=10.1145/1731888.1731890|issn=1931-8499}}</ref> with permission from Martin Butz)]]

'''Learning classifier systems''', or '''LCS''', are a paradigm of [[rule-based machine learning]] methods that combine a discovery component (e.g. typically a [[genetic algorithm]]) with a learning component (performing either [[supervised learning]], [[reinforcement learning]], or [[unsupervised learning]]).<ref name=":1">{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2009-09-22|title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap|journal=Journal of Artificial Evolution and Applications|language=en|volume=2009|pages=1–25|doi=10.1155/2009/736398|issn=1687-6229|doi-access=free}}</ref> Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a [[piecewise]] manner in order to make predictions (e.g. [[behavior modeling]],<ref>{{Cite journal|last=Dorigo|first=Marco|title=Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems|journal=Machine Learning|language=en|volume=19|issue=3|pages=209–240|doi=10.1007/BF00996270|issn=0885-6125|year=1995|doi-access=free}}</ref> [[Statistical classification|classification]],<ref>{{Cite journal|last=Bernadó-Mansilla|first=Ester|last2=Garrell-Guiu|first2=Josep M.|date=2003-09-01|title=Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks|journal=Evolutionary Computation|volume=11|issue=3|pages=209–238|doi=10.1162/106365603322365289|pmid=14558911|issn=1063-6560}}</ref><ref name=":0">{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2015-04-03|title=ExSTraCS 2.0: description and evaluation of a scalable learning classifier system|journal=Evolutionary Intelligence|language=en|volume=8|issue=2–3|pages=89–116|doi=10.1007/s12065-015-0128-8|issn=1864-5909|pmc=4583133|pmid=26417393}}</ref> [[data mining]],<ref name=":0" /><ref>{{Cite book|title=Advances in Learning Classifier Systems|url=https://archive.org/details/advanceslearning00lanz|url-access=limited|last=Bernadó|first=Ester|last2=Llorà|first2=Xavier|last3=Garrell|first3=Josep M.|date=2001-07-07|publisher=Springer Berlin Heidelberg|isbn=9783540437932|editor-last=Lanzi|editor-first=Pier Luca|series=Lecture Notes in Computer Science|pages=115–132[https://archive.org/details/advanceslearning00lanz/page/n120 115]–132|language=en|doi=10.1007/3-540-48104-4_8|editor-last2=Stolzmann|editor-first2=Wolfgang|editor-last3=Wilson|editor-first3=Stewart W.}}</ref><ref>{{Cite book|title=Learning Classifier Systems|url=https://archive.org/details/learningclassifi00kova_690|url-access=limited|last=Bacardit|first=Jaume|last2=Butz|first2=Martin V.|date=2007-01-01|publisher=Springer Berlin Heidelberg|isbn=9783540712305|editor-last=Kovacs|editor-first=Tim|series=Lecture Notes in Computer Science|pages=282–290[https://archive.org/details/learningclassifi00kova_690/page/n291 282]–290|language=en|doi=10.1007/978-3-540-71231-2_19|editor-last2=Llorà|editor-first2=Xavier|editor-last3=Takadama|editor-first3=Keiki|editor-last4=Lanzi|editor-first4=Pier Luca|editor-last5=Stolzmann|editor-first5=Wolfgang|editor-last6=Wilson|editor-first6=Stewart W.|citeseerx = 10.1.1.553.4679}}</ref> [[Regression analysis|regression]],<ref>{{Cite book|last=Urbanowicz|first=Ryan|last2=Ramanand|first2=Niranjan|last3=Moore|first3=Jason|date=2015-01-01|title=Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System|journal=Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation|series=GECCO Companion '15|___location=New York, NY, USA|publisher=ACM|pages=1029–1036|doi=10.1145/2739482.2768453|isbn=9781450334884}}</ref> [[function approximation]],<ref>{{Cite journal|last=Butz|first=M. V.|last2=Lanzi|first2=P. L.|last3=Wilson|first3=S. W.|date=2008-06-01|title=Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction|journal=IEEE Transactions on Evolutionary Computation|volume=12|issue=3|pages=355–376|doi=10.1109/TEVC.2007.903551|issn=1089-778X}}</ref> or [[Strategy (game theory)|game strategy]]). This approach allows complex [[Feasible region|solution spaces]] to be broken up into smaller, simpler parts.

The founding concepts behind learning classifier systems came from attempts to model [[complex adaptive system]]s, using rule-based agents to form an artificial cognitive system (i.e. [[artificial intelligence]]).