research-article

A multi-objective optimization design framework for ensemble generation

Authors:

Victor Henrique Alves Ribeiro,

Gilberto Reynoso-MezaAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1882 - 1885

https://doi.org/10.1145/3205651.3208219

Published: 06 July 2018 Publication History

Abstract

Machine learning algorithms have found to be useful for the solution of complex engineering problems. However, due to problem's characteristics, such as class imbalance, classical methods may not be formidable. The authors believe that the application of multi-objective optimization design can improve the results of machine learning algorithms on such scenarios. Thus, this paper proposes a novel methodology for the creation of ensembles of classifiers. To do so, a multi-objective optimization design approach composed of two steps is used. The first step focus on generating a set of diverse classifiers, while the second step focus on the selection of such classifiers as ensemble members. The proposed method is tested on a real-world competition data set, using both decision trees and logistic regression classifiers. Results show that the ensembles created with such technique outperform the best ensemble members.

References

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Cited By

Ranjan Lenka SKishoro Bisoy SPriyadarshini RLee Hui KSain M(2024)Evolutionary-Based Multi-Objective and Conditional Generative Adversarial Networks for Credit ScoringIEEE Access10.1109/ACCESS.2024.348690912(158346-158366)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3486909
Klikowska JWoźniak M(2024)Using Multi-Objective Optimization to build non-Random ForestLogic Journal of the IGPL10.1093/jigpal/jzae110Online publication date: 10-Sep-2024
https://doi.org/10.1093/jigpal/jzae110
Ribeiro VReynoso-Meza G(2023)Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring ProblemInternational Journal of Information Technology & Decision Making10.1142/S021962202350010423:01(447-474)Online publication date: 13-Feb-2023
https://doi.org/10.1142/S0219622023500104
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Index Terms

A multi-objective optimization design framework for ensemble generation
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Continuous space search
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms
      1. Ensemble methods

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cover image ACM Conferences

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Copyright © 2018 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: 06 July 2018

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GECCO '18

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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

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Cited By

Ranjan Lenka SKishoro Bisoy SPriyadarshini RLee Hui KSain M(2024)Evolutionary-Based Multi-Objective and Conditional Generative Adversarial Networks for Credit ScoringIEEE Access10.1109/ACCESS.2024.348690912(158346-158366)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3486909
Klikowska JWoźniak M(2024)Using Multi-Objective Optimization to build non-Random ForestLogic Journal of the IGPL10.1093/jigpal/jzae110Online publication date: 10-Sep-2024
https://doi.org/10.1093/jigpal/jzae110
Ribeiro VReynoso-Meza G(2023)Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring ProblemInternational Journal of Information Technology & Decision Making10.1142/S021962202350010423:01(447-474)Online publication date: 13-Feb-2023
https://doi.org/10.1142/S0219622023500104
Baldini LMartino ARizzi A(2022)A Multi-objective Optimization Approach for the Synthesis of Granular Computing-Based Classification Systems in the Graph DomainSN Computer Science10.1007/s42979-022-01260-43:6Online publication date: 9-Aug-2022
https://doi.org/10.1007/s42979-022-01260-4
Grzyb JWoźniak M(2022)SVM ensemble training for imbalanced data classification using multi-objective optimization techniquesApplied Intelligence10.1007/s10489-022-04291-953:12(15424-15441)Online publication date: 17-Nov-2022
https://doi.org/10.1007/s10489-022-04291-9
Mousavian Anaraki SHaeri AMoslehi F(2022)Generating balanced and strong clusters based on balance-constrained clustering approach (strong balance-constrained clustering) for improving ensemble classifier performanceNeural Computing and Applications10.1007/s00521-022-07595-634:23(21139-21155)Online publication date: 3-Aug-2022
https://doi.org/10.1007/s00521-022-07595-6
Jan ZVerma B(2021)Multicluster Class-Balanced EnsembleIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297983932:3(1014-1025)Online publication date: Mar-2021
https://doi.org/10.1109/TNNLS.2020.2979839
Asadi SRoshan S(2021)A bi-objective optimization method to produce a near-optimal number of classifiers and increase diversity in BaggingKnowledge-Based Systems10.1016/j.knosys.2020.106656213:COnline publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1016/j.knosys.2020.106656
Roshan SAsadi S(2021)Development of ensemble learning classification with density peak decomposition-based evolutionary multi-objective optimizationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-020-01271-8Online publication date: 7-Apr-2021
https://doi.org/10.1007/s13042-020-01271-8
Toutouh JHemberg EO'Reily UCoello Coello C(2020)Re-purposing heterogeneous generative ensembles with evolutionary computationProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390229(425-434)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390229
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