research-article

Evolving ensembles in multi-objective genetic programming for classification with unbalanced data

Authors:

Mengjie ZhangAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1331 - 1338

https://doi.org/10.1145/2001576.2001756

Published: 12 July 2011 Publication History

Abstract

Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this performance is usually better than many of its individual members.

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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
Benjamin JMathew J(2024)Enhancing continuous integration predictions: a hybrid LSTM-GRU deep learning framework with evolved DBSO algorithmComputing10.1007/s00607-024-01370-2107:1Online publication date: 26-Nov-2024
https://doi.org/10.1007/s00607-024-01370-2
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
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Index Terms

Evolving ensembles in multi-objective genetic programming for classification with unbalanced data
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Abstract
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Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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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
Benjamin JMathew J(2024)Enhancing continuous integration predictions: a hybrid LSTM-GRU deep learning framework with evolved DBSO algorithmComputing10.1007/s00607-024-01370-2107:1Online publication date: 26-Nov-2024
https://doi.org/10.1007/s00607-024-01370-2
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
Pei WXue BShang LZhang M(2022)High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and SelectionEvolutionary Computation10.1162/evco_a_0030430:1(99-129)Online publication date: 1-Mar-2022
https://doi.org/10.1162/evco_a_00304
Saidani IOuni AMkaouer M(2022)Detecting Continuous Integration Skip Commits Using Multi-Objective Evolutionary SearchIEEE Transactions on Software Engineering10.1109/TSE.2021.312916548:12(4873-4891)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TSE.2021.3129165
Saidani IOuni AChouchen MMkaouer MSpinellis DGousios GChechik MDi Penta M(2021)BF-detector: an automated tool for CI build failure detectionProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473115(1530-1534)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473115
Pei WXue BShang LZhang MKrawiec K(2021)Genetic programming for borderline instance detection in high-dimensional unbalanced classificationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459284(349-357)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459284
Pei WXue BShang LZhang M(2021)Developing Interval-Based Cost-Sensitive Classifiers by Genetic Programming for Binary High-Dimensional Unbalanced Classification [Research Frontier]IEEE Computational Intelligence Magazine10.1109/MCI.2020.303907016:1(84-98)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1109/MCI.2020.3039070
He HCoello Coello CTogelius JJin YOng YAbbass H(2021)CIS Publication Spotlight [Publication Spotlight]IEEE Computational Intelligence Magazine10.1109/MCI.2020.303906516:1(18-20)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1109/MCI.2020.3039065
Ting C(2021)The Wonders of Knowledge Transfer [Editor's Remarks]IEEE Computational Intelligence Magazine10.1109/MCI.2020.303902216:1(2-2)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1109/MCI.2020.3039022
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