skip to main content
10.1145/2330163.2330262acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

GP under streaming data constraints: a case for pareto archiving?

Published: 07 July 2012 Publication History

Abstract

Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000's to 100,000's of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results.

References

[1]
H. Abdulsalam, D. B. Skillicorn, and P. Martin. Classification using streaming random forests. IEEE Transactions on Knowledge and Data Engineering, 23(1):22--36, 2011.
[2]
J. Cartlidge and D. Ait-Boudaoud. Autonomous virulence adaptation improves coevolutionary optimization. IEEE Transactions on Evolutionary Computation, 15(2):215--229, 2011.
[3]
E. D. de Jong. A monolithic archive for pareto-coevolution. Evolutionary Computation, 15(1):61--93, 2007.
[4]
I. Dempsey, M. O'Neill, and A. Brabazon. Adaptive trading with grammatical evolution. In IEEE CEC, pages 2587--2592, 2006.
[5]
J. A. Doucette, A. R. McIntyre, P. Lichodzijewski, and M. I. Heywood. Symbolic coevolutionary genetic programming: A benchmarking study under large attribute spaces. Genetic Programming and Evolvable Machines, 13(1):71--101, 2012.
[6]
S. G. Ficici and J. Pollack. Pareto optimality in coevolutionary learning. In European Conference on Advances in Artificial Life, pages 316--325, 2001.
[7]
C. Gathercole and P. Ross. Dynamic training subset selection for supervised learning in genetic programming. In PPSN, pages 312--321, 1994.
[8]
M. Kotanchek, G. Smits, and E. Vladislavleva. Exploiting trustable models via pareto GP for targeted data collection. In Genetic Programming Theory and Practice VI, pages 145--162. Springer, 2009.
[9]
M. Lemczyk. Pareto-cevolutionary genetic programming classifier. Master's thesis, Faculty of Computer Science, Dalhousie University, 2006. http://web.cs.dal.ca/ mheywood/Thesis/MCS.html.
[10]
P. Lichodzijewski and M. I. Heywood. Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification. In ACM GECCO, pages 464--471, 2007.
[11]
P. Lichodzijewski and M. I. Heywood. Managing team-based problem solving with symbiotic bid-based genetic programming. In ACM GECCO, pages 363--370, 2008.
[12]
A. R. McIntyre and M. I. Heywood. Cooperative problem decomposition in pareto competitive classifier models of coevolution. In European Conference on Genetic Programming, pages 289--300, 2008.
[13]
R. W. Morrison. Designing evolutionary algorithms for dynamic environments. Springer, 2004.
[14]
J. Noble and R. A. Watson. Pareto coevolution: Using performance against coevolved opponents in a game as dimensions for pareto selection. In ACM GECCO, pages 493--500, 2001.
[15]
M. O'Neill, L. Vanneschi, S. Gustafson, and W. Banzhaf. Open issues in genetic programming. Genetic Programming and Evolvable Machines, 11(3):339--363, 2010.
[16]
N. Wagner, Z. Michalewicz, M. Khouja, and R. R. McGregor. Time series forecasting for dynamic environments: The DyFor genetic program model. IEEE Transactions on Evolutionary Computation, 11(4):433--452, 2007.
[17]
S. X. Wu and W. Banzhaf. Rethinking multilevel selection in genetic programming. In ACM GECCO, pages 1403--1410, 2011.
[18]
X. Zhu, P. Zhang, X. Lin, and Y. Shi. Active learning from stream data using optimal weight classifier ensemble. IEEE Transactions on Systems, Man, and Cybernetics--Part B, 40(6):1607--1621, 2010.

Cited By

View all
  • (2018)On botnet detection with genetic programming under streaming data label budgets and class imbalanceSwarm and Evolutionary Computation10.1016/j.swevo.2017.09.00839(123-140)Online publication date: Apr-2018
  • (2017)Properties of a GP active learning framework for streaming data with class imbalanceProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071213(945-952)Online publication date: 1-Jul-2017
  • (2016)Classifying streaming data using grammar-based immune programming2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7849969(1-8)Online publication date: Dec-2016
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation
July 2012
1396 pages
ISBN:9781450311779
DOI:10.1145/2330163
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. coevolution
  2. genetic programming
  3. online learning
  4. pareto archiving
  5. streaming data

Qualifiers

  • Research-article

Conference

GECCO '12
Sponsor:
GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 14 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2018)On botnet detection with genetic programming under streaming data label budgets and class imbalanceSwarm and Evolutionary Computation10.1016/j.swevo.2017.09.00839(123-140)Online publication date: Apr-2018
  • (2017)Properties of a GP active learning framework for streaming data with class imbalanceProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071213(945-952)Online publication date: 1-Jul-2017
  • (2016)Classifying streaming data using grammar-based immune programming2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7849969(1-8)Online publication date: Dec-2016
  • (2015)Evolutionary model building under streaming data for classification tasksGenetic Programming and Evolvable Machines10.1007/s10710-014-9236-y16:3(283-326)Online publication date: 1-Sep-2015
  • (2015)Evolving GP Classifiers for Streaming Data Tasks with Concept Change and Label Budgets: A Benchmarking StudyHandbook of Genetic Programming Applications10.1007/978-3-319-20883-1_18(451-480)Online publication date: 2015
  • (2015)Tapped Delay Lines for GP Streaming Data Classification with Label BudgetsGenetic Programming10.1007/978-3-319-16501-1_11(126-138)Online publication date: 15-Mar-2015
  • (2014)On the application of GP to streaming data classification tasks with label budgetsProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2611385(1287-1294)Online publication date: 12-Jul-2014
  • (2013)Benchmarking pareto archiving heuristics in the presence of concept driftProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463489(885-892)Online publication date: 6-Jul-2013
  • (2013)On GPU based fitness evaluation with decoupled training partition cardinalityProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_49(489-498)Online publication date: 3-Apr-2013
  • (2013)On the utility of trading criteria based retraining in forex marketsProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_20(192-202)Online publication date: 3-Apr-2013

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media