research-article

Toward curious learning classifier systems: combining XCS with active learning concepts

Authors:

Jörg HähnerAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1349 - 1356

https://doi.org/10.1145/3067695.3082488

Published: 15 July 2017 Publication History

Abstract

This paper proposes a novel approach to enhance the rather reactive knowledge generation process in Learning Classifier Systems (LCS) toward a more proactive means. We describe how concepts from the domain of Active Learning can be adapted to XCS's algorithmic structure to introduce 'curiosity'. The overall goal is to allow LCSs to build up new knowledge before it is actually requested during the online learning process. We deem such a methodology meaningful in scenarios where data samples are distributed non-uniformly and partially sparse over the input space. Such data imbalances result in gaps within the knowledge base, i.e. an LCS population. We underpin the general potential of our approaches by presenting preliminary results on a realistic data set from the domain of medical diagnosis as well as on a novel toy problem.

References

[1]

Dana Angluin. 1988. Queries and Concept Learning. Machine Learning 2, 4 (1988), 319--342.

Digital Library

[2]

E. Bernad-Mansilla and J. M. Garrell-Guiu. 2003. Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evol. Comput. 11, 3 (2003), 209--238.

Digital Library

[3]

L. Bull, J. Sha'Aban, A. Tomlinson, J.D. Addison, and B.G. Heydecker. 2004. Towards Distributed Adaptive Control for Road Traffic Junction Signals using Learning Classifier Systems. In Applications of Learning Classifier Systems. Studies in Fuzziness and Soft Computing. Vol. 150. Springer, 276--299.

[4]

M.V. Butz, T. Kovacs, P.L. Lanzi, and S.W. Wilson. 2004. Toward a Theory of Generalization and Learning in XCS. Evol. Comp., IEEE Trans. 8, 1 (2004), 28--46.

Digital Library

[5]

Martin V. Butz and Olivier Sigaud. 2012. XCSF with local deletion: preventing detrimental forgetting. Evolutionary Intelligence 5, 2 (2012), 117--127.

[6]

Martin Butz and Stewart W. Wilson. 2002. An Algorithmic Description of XCS. Soft Comput. 6, 3--4 (2002), 144--153.

[7]

David Cohn, Les Atlas, and Richard Ladner. 1994. Improving generalization with active learning. Machine Learning 15, 2 (1994), 201--221.

Digital Library

[8]

David E. Goldberg. 1987. Computer-aided pipeline operation using genetic algorithms and rule learning. PART II: Rule learning control of a pipeline under normal and abnormal conditions. Engineering with Computers 3, 1 (1987), 47--58.

[9]

H. He and E. A. Garcia. 2009. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 21, 9 (Sept 2009), 1263--1284.

Digital Library

[10]

John H. Holland. 1976. Adaptation. In Progress in Theoretical Biology, Robert Rosen and F.M. Snell (Eds.). Vol. 4. Academic Press, New York, 263--293.

[11]

Faten Kharbat, Mohammed Odeh, and Larry Bull. 2008. Knowledge Discovery from Medical Data: An Empirical Study with XCS. In Learning Classifier Systems in Data Mining. Studies in Computational Intelligence, Vol. 125. Springer, 93--121.

[12]

Tim Kovacs. 2000. Strength or Accuracy? Fitness Calculation in Learning Classifier Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 143--160.

Digital Library

[13]

J. Lehman and K. Stanley. 2008. Exploiting open-endedness to solve problems through the search for novelty. In Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. MIT Press, Cambridge, MA, 329--336.

[14]

David D. Lewis and William A. Gale. 1994. A Sequential Algorithm for Training Text Classifiers. In Proc. of 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3--12.

Digital Library

[15]

M. Lichman. 2013. UCI Machine Learning Repository. (2013). http://archive.ics.uci.edu/ml

[16]

Olvi L. Mangasarian, W. Nick Street, and William H. Wolberg. 1995. Breast Cancer Diagnosis and Prognosis Via Linear Programming. Operations Research 43, 4 (1995), 570--577.

Digital Library

[17]

Anis Najar, Olivier Sigaud, and Mohamed Chetouani. 2015. Social-Task Learning for HRI. Springer International Publishing, Cham, 472--481.

[18]

Anis Najar, Olivier Sigaud, and Mohamed Chetouani. 2015. Socially Guided XCS: Using Teaching Signals to Boost Learning. In Proceedings of the Companion Publication of GECCO'15 (GECCO Companion '15). ACM, NY, USA, 1021--1028.

Digital Library

[19]

M. Nakata, P. L. Lanzi, T. Kovacs, W. N. Browne, and K. Takadama. 2015. How should learning classifier systems cover a state-action space?. In 2015 LEEE Congress on Evolutionary Computation (CEC). 3012--3019.

[20]

Nintendo. 1985. Super Mario Bros. Video Game for the Nintendo Entertainment System, Japan. (1985).

[21]

Albert Orriols-Puig and Ester Bernadó-Mansilla. 2008. Evolutionary rule-based systems for imbalanced data sets. Soft Computing 13, 3 (2008), 213.

Digital Library

[22]

H. Prothmann, F. Rochner, S. Tomforde, J. Branke, C. Müller-Schloer, and H. Schmeck. 2008. Organic Control of Traffic Lights. In Proc. of the 5th Int. Conf. on Autonomic and Trusted Computing. Springer, Berlin, Heidelberg, 219--233.

Digital Library

[23]

Tobias Reitmaier, Adrian Calma, and Bernhard Sick. 2015. Transductive active learning-A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Inf. Sci. 293 (2015), 275--298.

[24]

Stefan Rudolph, Sarah Edenhofer, Sven Tomforde, and Jörg Hähner. 2014. Reinforcement Learning for Coverage Optimization Through PTZ Camera Alignment in Highly Dynamic Environments. In Proceedings of the International Conference on Distributed Smart Cameras (ICDSC '14). ACM, NY, USA, Article 19.

Digital Library

[25]

B. Settles. 2009. Active Learning Literature Survey. Technical Report 1648. University of Wisconsin-Madison.

Digital Library

[26]

H. S. Seung, M. Opper, and H. Sompolinsky. 1992. Query by Committee. In Proc. of the 5th Annual Workshop on Computational Learning Theory (COLT '92). ACM, NY, USA, 287--294.

Digital Library

[27]

C. E. Shannon. 1948. A Mathematical Theory of Communication. Bell System Technical Journal 27, 3 (1948), 379--423.

[28]

Patrick O. Stalph and Martin V. Butz. 2012. Learning local linear Jacobians for flexible and adaptive robot arm control. Genet Program Evolvable Mach 13, 2 (2012), 137--157.

Digital Library

[29]

A. Stein, C. Eymüller, D. Rauh, S. Tomforde, and J. Hähner. 2016. Interpolation-based Classifier Generation in XCSF. In 2016 LEEE Congress on Evolutionary Computation (CEC). 3990--3998.

[30]

A. Stein, D. Rauh, S. Tomforde, and J. Hähner. 2017. Interpolation in the extended Classifier System: An Architectural Perspective. Journal of Systems Architecture (2017).

Digital Library

[31]

Christopher Stone and Larry Bull. 2003. For Real! XCS with Continuous-Valued Inputs. Evol. Comput. 11, 3 (2003), 298--336.

Digital Library

[32]

Ryan J. Urbanowicz, Delaney Granizo-Mackenzie, and Jason H. Moore. 2012. Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity. Springer Berlin Heidelberg, 266--275.

Digital Library

[33]

Ryan J. Urbanowicz and Jason H. Moore. 2015. ExSTraCS 2.0: description and evaluation of a scalable learning classifier system. Evolutionary Intelligence 8, 2 (2015), 89--116.

[34]

Geoffrey I. Webb, Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean. 2016. Characterizing concept drift. Data Min. Knowl. Discov. (2016), 1--31.

Digital Library

[35]

Gary M. Weiss. 2004. Mining with Rarity: A Unifying Framework. SIGKDD Explor. Newsl. 6. 1 (June 2004), 7--19.

Digital Library

[36]

Stewart W. Wilson. 1994. ZCS: A Zeroth Level Classifier System. Evol. Comput. 2, 1 (1994), 1--18.

Digital Library

[37]

Stewart W. Wilson. 1995. Classifier Fitness Based on Accuracy. Evol. Comput. 3, 2 (1995). 149--175.

Digital Library

[38]

Stewart W. Wilson. 2000. Get Real! XCS with Continuous-Valued Inputs. In Learning Classifier Systems. Lecture Notes in Computer Science, Vol. 1813. Springer Berlin Heidelberg, 209--219.

Digital Library

[39]

Stewart W. Wilson. 2001. Mining Oblique Data with XCS. In Advances in Learning Classifier Systems. Lecture Notes in Computer Science, Vol. 1996. Springer Berlin Heidelberg, 158--174.

Digital Library

[40]

Stewart W. Wilson. 2002. Classifiers that Approximate Functions. Natural Computing 1, 2--3 (2002), 211--234.

Digital Library

[41]

Qiong Wu and Chunyan Miao. 2013. Curiosity: From Psychology to Computation. ACM Comput. Surv. 46, 2, Article 18 (Dec. 2013), 26 pages.

Digital Library

Cited By

Pei WXue BZhang MShang LYao XZhang Q(2024)A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?IEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325723028:2(353-373)Online publication date: Apr-2024
https://doi.org/10.1109/TEVC.2023.3257230
Heider MStegherr HNordsieck RHähner J(2023)Assessing Model Requirements for Explainable AI: A Template and Exemplary Case StudyArtificial Life10.1162/artl_a_0041429:4(468-486)Online publication date: 1-Nov-2023
https://doi.org/10.1162/artl_a_00414
Görlich-Bucher MHähner JWagner M(2022)XCSF under limited supervisionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534046(2080-2085)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534046
Show More Cited By

Index Terms

Toward curious learning classifier systems: combining XCS with active learning concepts
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Active learning settings
    2. Machine learning approaches
      1. Rule learning

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 July 2017

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GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Pei WXue BZhang MShang LYao XZhang Q(2024)A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?IEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325723028:2(353-373)Online publication date: Apr-2024
https://doi.org/10.1109/TEVC.2023.3257230
Heider MStegherr HNordsieck RHähner J(2023)Assessing Model Requirements for Explainable AI: A Template and Exemplary Case StudyArtificial Life10.1162/artl_a_0041429:4(468-486)Online publication date: 1-Nov-2023
https://doi.org/10.1162/artl_a_00414
Görlich-Bucher MHähner JWagner M(2022)XCSF under limited supervisionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534046(2080-2085)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534046
Shiraishi HHayamizu YSato HTakadama KWagner M(2022)Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528841(422-430)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528841
Wagner AStein A(2022)Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input SpacesSN Computer Science10.1007/s42979-022-01060-w3:2Online publication date: 28-Feb-2022
https://doi.org/10.1007/s42979-022-01060-w
Stein ATomforde S(2021)Reflective Learning Classifier Systems for Self-Adaptive and Self-Organising Agents2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C52956.2021.00043(139-145)Online publication date: Sep-2021
https://doi.org/10.1109/ACSOS-C52956.2021.00043
Wagner AStein A(2021)On the Effects of Absumption for XCS with Continuous-Valued InputsApplications of Evolutionary Computation10.1007/978-3-030-72699-7_44(697-713)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_44
Stein AMaier RRosenbauer LHähner JCoello Coello C(2020)XCS classifier system with experience replayProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390249(404-413)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390249
Stein AAuger AStützle T(2019)Learning classifier systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323393(747-769)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3323393
Stein ATomforde SDiaconescu AHahner JMuller-Schloer C(2018)A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)10.1109/FAS-W.2018.00048(204-213)Online publication date: Sep-2018
https://doi.org/10.1109/FAS-W.2018.00048
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