research-article

The benefits of using multi-objectivization for mining pittsburgh partial classification rules in imbalanced and discrete data

Authors:

Julien Taillard,

Laetitia Jourdan,

Clarisse DhaenensAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 543 - 550

https://doi.org/10.1145/2463372.2463432

Published: 06 July 2013 Publication History

Abstract

A large number of rule interestingness measures have been used as objectives in multi-objective classification rule mining algorithms. Aggregation or Pareto dominance are commonly used to deal with these multiple objectives. This paper compares these approaches on a partial classification problem over discrete and imbalanced data. After performing a Principal Component Analysis (PCA) to select candidate objectives and find conflictive ones, the two approaches are evaluated. The Pareto dominance-based approach is implemented as a dominance-based local search (DMLS) algorithm using confidence and sensitivity as objectives, while the other is implemented as a single-objective hill climbing using F-Measure as an objective, which combines confidence and sensitivity. Results shows that the dominance-based approach obtains statistically better results than the single-objective approach.

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Cited By

Cacereño AGreiner DZuñiga AGalván B(2024)Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach ExplorationJournal of Engineering10.1155/2024/93905452024:1Online publication date: 6-Jan-2024
https://doi.org/10.1155/2024/9390545
Dhaenens CJourdan L(2022)Metaheuristics for data mining: survey and opportunities for big dataAnnals of Operations Research10.1007/s10479-021-04496-0314:1(117-140)Online publication date: 18-Jan-2022
https://doi.org/10.1007/s10479-021-04496-0
Tari SSzczepanski NMousin LJacques JKessaci MJourdan L(2020)Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185785(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185785
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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 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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Published: 06 July 2013

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

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

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Cited By

Cacereño AGreiner DZuñiga AGalván B(2024)Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach ExplorationJournal of Engineering10.1155/2024/93905452024:1Online publication date: 6-Jan-2024
https://doi.org/10.1155/2024/9390545
Dhaenens CJourdan L(2022)Metaheuristics for data mining: survey and opportunities for big dataAnnals of Operations Research10.1007/s10479-021-04496-0314:1(117-140)Online publication date: 18-Jan-2022
https://doi.org/10.1007/s10479-021-04496-0
Tari SSzczepanski NMousin LJacques JKessaci MJourdan L(2020)Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185785(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185785
Tari SHoos HJacques JKessaci MJourdan L(2020)Automatic Configuration of a Multi-objective Local Search for Imbalanced ClassificationParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58112-1_5(65-77)Online publication date: 31-Aug-2020
https://doi.org/10.1007/978-3-030-58112-1_5
Tari SMousin LJacques JKessaci MJourdan L(2020)Impact of the Discretization of VOCs for Cancer Prediction Using a Multi-Objective AlgorithmLearning and Intelligent Optimization10.1007/978-3-030-53552-0_16(151-157)Online publication date: 18-Jul-2020
https://doi.org/10.1007/978-3-030-53552-0_16
Dhaenens CJourdan L(2019)Metaheuristics for data mining4OR10.1007/s10288-019-00402-4Online publication date: 6-Apr-2019
https://doi.org/10.1007/s10288-019-00402-4
Dhaenens CJourdan L(2016)BibliographyMetaheuristics for Big Data10.1002/9781119347569.biblio(161-186)Online publication date: 17-Sep-2016
https://doi.org/10.1002/9781119347569.biblio
Segura CCoello CMiranda GLeón C(2015)Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimizationAnnals of Operations Research10.1007/s10479-015-2017-z240:1(217-250)Online publication date: 22-Sep-2015
https://doi.org/10.1007/s10479-015-2017-z

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