- Author:
- Jan Drugowitsch
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question "What is an LCS supposed to learn?". Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book for illustrative purposes closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework.
Cited By
Pätzel D, Heider M and Hähner J Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms Proceedings of the Companion Conference on Genetic and Evolutionary Computation, (1657-1662)- Pätzel D and Hähner J The Bayesian learning classifier system Proceedings of the Genetic and Evolutionary Computation Conference, (413-421)
- Pätzel D, Stein A and Hähner J A survey of formal theoretical advances regarding XCS Proceedings of the Genetic and Evolutionary Computation Conference Companion, (1295-1302)
- Pätzel D and Hähner J An algebraic description of XCS Proceedings of the Genetic and Evolutionary Computation Conference Companion, (1434-1441)
- Nakata M, Browne W, Hamagami T and Takadama K Theoretical XCS parameter settings of learning accurate classifiers Proceedings of the Genetic and Evolutionary Computation Conference, (473-480)
- Kuber K, Card S, Mehrotra K and Mohan C Rule networks in learning classifier systems Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, (977-982)
- Lanzi P Learning classifier systems Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, (407-430)
- Browne W and Urbanowicz R Learning classifier systems Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, (439-468)
- Iqbal M, Browne W and Zhang M Extracting and using building blocks of knowledge in learning classifier systems Proceedings of the 14th annual conference on Genetic and evolutionary computation, (863-870)
- Butz M Learning classifier systems Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, (941-962)
- Franco M, Krasnogor N and Bacardit J Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding Proceedings of the 13th annual conference on Genetic and evolutionary computation, (1291-1298)
- Edakunni N, Brown G and Kovacs T Online, GA based mixture of experts Proceedings of the 13th annual conference on Genetic and evolutionary computation, (1267-1274)
- Kovacs T, Edakunni N and Brown G Accuracy exponentiation in UCS and its effect on voting margins Proceedings of the 13th annual conference on Genetic and evolutionary computation, (1251-1258)
- Butz M Learning classifier systems Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, (2331-2352)
- Barreto A, Augusto D and Barbosa H On the characteristics of sequential decision problems and their impact on evolutionary computation and reinforcement learning Proceedings of the 9th international conference on Artificial evolution, (194-205)
- Edakunni N, Kovacs T, Brown G and Marshall J Modeling UCS as a mixture of experts Proceedings of the 11th Annual conference on Genetic and evolutionary computation, (1187-1194)
- Urbanowicz R and Moore J (2009). Learning classifier systems, Journal of Artificial Evolution and Applications, 2009, (1-25), Online publication date: 1-Jan-2009.
Index Terms
- Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
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