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Overfitting in the selection of classifier ensembles: a comparative study between PSO and GA

Authors:

Eulanda M. Dos Santos,

Luiz S. Oliveira,

Robert Sabourin,

Patrick MaupinAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1423 - 1424

https://doi.org/10.1145/1389095.1389370

Published: 12 July 2008 Publication History

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Abstract

Classifier ensemble selection may be formulated as a learning task since the search algorithm operates by minimizing/maximizing the objective function. As a consequence, the selection process may be prone to overfitting. The objectives of this paper are: (1) to show how overfitting can be detected when the selection is performed by two classical search algorithms: Genetic Algorithm and Particle Swarm Optimization; and (2) to verify which algorithm is more prone to overfitting. The experimental results demonstrate that GA appears to be more affected by overfitting.

References

[1]

L. Oliveira, R. Sabourin, F. Bortolozzi, and C. Suen. Automatic recognition of handwritten numerical strings: A recognition and verification strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11):1438--1454, 2002.

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[2]

P. Radtke, R. Sabourin, and T. Wong. Impact of solution over-fit on evolutionary multi-objective optimization for classification systems. In Proceedings of the IEEE International Joint Conference on Neural Network, Vancouver, Canada, 2006.

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[3]

J. Reunanen. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research, 3:1371--1382, 2003.

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Cited By

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Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
https://doi.org/10.1017/S0269888923000024
Zhao YHryniewicki MCheng FFu BZhu X(2018)Employee Turnover Prediction with Machine Learning: A Reliable ApproachIntelligent Systems and Applications10.1007/978-3-030-01057-7_56(737-758)Online publication date: 8-Nov-2018
https://doi.org/10.1007/978-3-030-01057-7_56
Ciresan DMeier UGambardella LSchmidhuber J(2011)Convolutional Neural Network Committees for Handwritten Character ClassificationProceedings of the 2011 International Conference on Document Analysis and Recognition10.1109/ICDAR.2011.229(1135-1139)Online publication date: 18-Sep-2011
https://dl.acm.org/doi/10.1109/ICDAR.2011.229
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Index Terms

Overfitting in the selection of classifier ensembles: a comparative study between PSO and GA
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
https://doi.org/10.1017/S0269888923000024
Zhao YHryniewicki MCheng FFu BZhu X(2018)Employee Turnover Prediction with Machine Learning: A Reliable ApproachIntelligent Systems and Applications10.1007/978-3-030-01057-7_56(737-758)Online publication date: 8-Nov-2018
https://doi.org/10.1007/978-3-030-01057-7_56
Ciresan DMeier UGambardella LSchmidhuber J(2011)Convolutional Neural Network Committees for Handwritten Character ClassificationProceedings of the 2011 International Conference on Document Analysis and Recognition10.1109/ICDAR.2011.229(1135-1139)Online publication date: 18-Sep-2011
https://dl.acm.org/doi/10.1109/ICDAR.2011.229
Dos Santos ESabourin RMaupin P(2009)Overfitting cautious selection of classifier ensembles with genetic algorithmsInformation Fusion10.1016/j.inffus.2008.11.00310:2(150-162)Online publication date: Apr-2009
https://doi.org/10.1016/j.inffus.2008.11.003
Lienemann KPlötz TFink G(2009)Stacking for Ensembles of Local Experts in Metabonomic ApplicationsProceedings of the 8th International Workshop on Multiple Classifier Systems10.1007/978-3-642-02326-2_50(498-508)Online publication date: 10-Jun-2009
https://dl.acm.org/doi/10.1007/978-3-642-02326-2_50

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Overfitting cautious selection of classifier ensembles with genetic algorithms

Optimal resampling and classifier prototype selection in classifier ensembles using genetic algorithms

Optimal resampling and classifier prototype selection in classifier ensembles using genetic algorithms

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