untitled
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<identifier>oai:HAL:hal-00520609v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Prediction using Pittsburgh learning classifier systems: APCS use case</title>
<creator>Peroumalnaïk, Mathias</creator>
<creator>Gilles, Enée</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Proceedings of the 12th annual conference comp on Genetic and evolutionary computation</source>
<source>Genetic And Evolutionary Computation Conference</source>
<coverage>Portland,Oregon, United States</coverage>
<contributor>ACM</contributor>
<publisher>ACM</publisher>
<identifier>hal-00520609</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00520609</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00520609</source>
<source>ACM. Genetic And Evolutionary Computation Conference, Jul 2010, Portland,Oregon, United States. ACM, pp.1901-1908, 2010, 〈10.1145/1830761.1830823〉</source>
<identifier>DOI : 10.1145/1830761.1830823</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1145/1830761.1830823</relation>
<language>en</language>
<subject lang=en>Pittsburgh learning classifier systems</subject>
<subject lang=en>apcs</subject>
<subject lang=en>classification</subject>
<subject lang=en>map</subject>
<subject lang=en>segmentation</subject>
<subject>I.2.6 Learning</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.</description>
<date>2010-07-06</date>
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