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Revisit of Rule-Deletion Strategy for XCSAM Classifier System on Classification

Masaya Nakata, Tomoki Hamagami
2017 Transactions of the Institute of Systems Control and Information Engineers  
The XCSAM classifier system is an approach of evolutionary rule-based machine learning, which evolves rules advocating the highest-return actions at state, resulting in best classification.  ...  This paper starts with claiming a limitation that XCSAM still fails to evolutionary generate adequate rules advocating the highest-return actions.  ...  Then, rather than building the pure best action map, a new concept of learning strategy which combines best action map with complete action map, may be a good solution to overcome the limitations of both  ... 
doi:10.5687/iscie.30.273 fatcat:eba2jzyvszdabfeqwyllsflp44

Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks

Ester Bernadó-Mansilla, Josep M. Garrell-Guiu
2003 Evolutionary Computation  
Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently.  ...  While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme.  ...  Figure 1 : 1 Proportion of the best action map covered by XCS (left) and UCS 4 : 4 Best action map (first column) and complete action map (all columns) of pos6.  ... 
doi:10.1162/106365603322365289 pmid:14558911 fatcat:25wu53rxhbe27mzk5li6nmvs3a

State of XCS Classifier System Research [chapter]

Stewart W. Wilson
2000 Lecture Notes in Computer Science  
XCS is a new kind of learning classifier system that differs from the traditional one primarily in its definition of classifier fitness and its relation to contemporary reinforcement learning.  ...  This paper reviews recent research on XCS with respect to representation, predictive modeling, internal state, noise, and underlying theory and technique.  ...  If XCS were to learn the mapping X x A ⇒ Y, with elements (x,a) → y, it could, from any present state x, consider chains of actions extending into the future; that is, it could plan.  ... 
doi:10.1007/3-540-45027-0_3 fatcat:uz5yxxs6mre4pjnxbvhi4selhe

Three-cornered coevolution learning classifier systems for classification tasks

Syahaneim Marzukhi, Will N. Browne, Mengjie Zhang
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
agent and two classification agents) learn and adapt to the changes of the problems without human involvement.  ...  This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation  ...  XCS, XCSR, UCS, A-PLUS). In order to enhance the capability of the LCSs model, Tabu Search is introduced.  ... 
doi:10.1145/2576768.2598235 dblp:conf/gecco/MarzukhiBZ14 fatcat:6fy5jzcd6fbjjf5kdosuxbocta

Metadata to Support Next-Generation Library Resource Discovery: Lessons from the eXtensible Catalog, Phase 1

Jennifer Bowen
2008 Information Technology and Libraries  
The strategies that the XC Project Team and XC Partner Institutions will use to address these issues can contribute to an agenda for attention and action within the library community to ensure that library  ...  The eXtensible Catalog (XC) Project at the University of Rochester will design and develop a set of open-source applications to provide libraries with an alternative way to reveal their collections to  ...  The metadata services hub architecture for XC is capable of handling the ingest and processing of metadata supplied by commercial content providers by adding additional services to handle the necessary  ... 
doi:10.6017/ital.v27i2.3253 fatcat:suslwerc4jhnra4pveinvdlypi

Guided Rule Discovery in XCS for High-Dimensional Classification Problems [chapter]

Mani Abedini, Michael Kirley
2011 Lecture Notes in Computer Science  
XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules.  ...  We introduce a new guided rule discovery mechanisms for XCS, inspired by feature selection techniques commonly used in machine learning.  ...  Our model (GRD-XCS), is inspired by feature selection techniques commonly used in machine learning.  ... 
doi:10.1007/978-3-642-25832-9_1 fatcat:vzwbps2stjduja5ocwjy4byobq

Learning Classifier Systems: A Brief Introduction [chapter]

Larry Bull
2004 Studies in Fuzziness and Soft Computing  
Learning] Classifier systems are a kind of rule-based system with general mechanisms for processing rules in parallel, for adaptive generation of new rules, and for testing the effectiveness of existing  ...  These mechanisms make possible performance and learning without the "brittleness" characteristic of most expert systems in AI. Holland et al., Induction, 1986  ...  Thanks also to my fellow members of the Learning Classifier Systems Group at UWE for so many interesting discussions.  ... 
doi:10.1007/978-3-540-39925-4_1 fatcat:jagauopuwnawhem46dbg5yu2py

The stability of long action chains in XCS

A. M. Barry
2002 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Whilst these findings have shed some light on the ability of XCS to form long action-chains, they have not provided a systematic and, above all, controlled investigation of the limits of XCS learning within  ...  Alwyn.Barry@uwe.ac.uk (++44) [0]117 344 3135 XCS [1][2] represents a new form of Learning Classifier System [3] that uses accuracy as a means of guiding fitness for selection within a Genetic Algorithm  ...  The first stage will seek to provide base-line results to demonstrate that XCS is capable of learning the stable payoff for each of the actions in each state of the environment.  ... 
doi:10.1007/s005000100115 fatcat:akplsycohzbehkuoq45jhu4hiq

XCS Classifier System with Experience Replay [article]

Anthony Stein, Roland Maier, Lukas Rosenbauer, Jörg Hähner
2020 arXiv   pre-print
XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks.  ...  To bridge this gap, this paper investigates the benefits of extending XCS with ER.  ...  Q-Learning learns its optimal policy by building a so-called state-action value function, or Q-function.  ... 
arXiv:2002.05628v1 fatcat:vc3kdnnh5jetdmwgjtbv2syyuq

Knowledge Representation in Learning Classifier Systems: A Review [article]

Farzaneh Shoeleh, Mahshid Majd, Ali Hamzeh, Sattar Hashemi
2015 arXiv   pre-print
Knowledge representation is a key component to the success of all rule based systems including learning classifier systems (LCSs).  ...  Furthermore, a precise explanation on the way that each technique partitions the problem space along with the extensive experimental results is provided.  ...  As experiments notified, this new enhanced XCSm can learn an optimal solution.  ... 
arXiv:1506.04002v1 fatcat:mpwk7ga3azbh5akoe3coaydp2q

Data Mining using Learning Classifier Systems [chapter]

Alwyn Barry, John Holmes, Xavier Llorà
2004 Studies in Fuzziness and Soft Computing  
Complexity -XCS learns using reward prediction values (Reinforcement Learning).  ...  Summary and Further Work Overall, it is now clear that XCS provided an extremely capable LCS platform from which to develop the toolset.  ... 
doi:10.1007/978-3-540-39925-4_2 fatcat:psic3csbjfh7zn4gx5alwebppa

Evolutionary Online Data Mining: An Investigation in a Dynamic Environment [chapter]

Hai H. Dam, Chris Lokan, Hussein A. Abbass
2007 Studies in Computational Intelligence  
Our results show that XCS is capable of recovering quickly from small changes in the underlying concepts. However, it requires significant time to re-learn a model after severe changes.  ...  Online or incremental learning becomes more important than ever for dealing with these problems.  ...  The adaptive learning approach is the next best.  ... 
doi:10.1007/978-3-540-49774-5_7 fatcat:ranrejqwtndn3ou7ubdyti36oq

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

Hsuan-Ta Lin, Po-Ming Lee, Tzu-Chien Hsiao
2015 The Scientific World Journal  
Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available.  ...  The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones.  ...  Acknowledgments This work was fully supported by the Taiwan Ministry of Science and Technology under Grants no MOST 103-2221-E-009-139.  ... 
doi:10.1155/2015/352895 pmid:26065018 pmcid:PMC4439506 fatcat:p5iibjay25fr5mzg6ut3etebqe

Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Ryan J. Urbanowicz, Jason H. Moore
2009 Journal of Artificial Evolution and Applications  
If complexity is your problem, learning classifier systems (LCSs) may offer a solution.  ...  These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence.  ...  This latter methodology which seeks a rule set of efficient generalizations tends to form a best action map (or a partial map) [102, 167] .  ... 
doi:10.1155/2009/736398 fatcat:dvubeerbl5ez3ed553sn2snofy

An inter-market arbitrage trading system based on extended classifier systems

Yu-Chia Hsu, An-Pin Chen, Jia-Haur Chang
2011 Expert systems with applications  
XCS is then adopted for knowledge rule discovery.  ...  Traditionally, the most popular arbitrage strategy is derived from the cost of carry model or by using the econometrics approach.  ...  The XCS-based model for arbitrage Extended classifier system The classifier system is an adaptive rule-base system consisting of enhanced learning mechanisms and the genetic algorithm, which is capable  ... 
doi:10.1016/j.eswa.2010.09.039 fatcat:iunzs53v6bechj7sfamr47toia
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