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A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm

Authors:

Fernando E.B. OteroAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 13 - 20

https://doi.org/10.1145/2908812.2908900

Published: 20 July 2016 Publication History

Abstract

In this paper, we introduce Ant-MinerMA to tackle mixed-attribute classification problems. Most classification problems involve continuous, ordinal and categorical attributes. The majority of Ant Colony Optimization (ACO) classification algorithms have the limitation of being able to handle categorical attributes only, with few exceptions that use a discretisation procedure when handling continuous attributes either in a preprocessing stage or during the rule creation. Using a solution archive as a pheromone model, inspired by the ACO for mixed-variable optimization (ACO-MV), we eliminate the need for a discretisation procedure and attributes can be treated directly as continuous, ordinal, or categorical. We compared the proposed Ant-MinerMA against cAnt-Miner, an ACO-based classification algorithm that uses a discretisation procedure in the rule construction process. Our results show that Ant-MinerMA achieved significant improvements on computational time due to the elimination of the discretisation procedure without affecting the predictive performance.

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Cited By

Kuo RWu CKuo T(2024)An ensemble method with a hybrid of genetic algorithm and K-prototypes algorithm for mixed data classificationComputers & Industrial Engineering10.1016/j.cie.2024.110066190(110066)Online publication date: Apr-2024
https://doi.org/10.1016/j.cie.2024.110066
Ou GHe YFournier-Viger PHuang J(2022)A Novel Mixed-Attribute Fusion-Based Naive Bayesian ClassifierApplied Sciences10.3390/app12201044312:20(10443)Online publication date: 17-Oct-2022
https://doi.org/10.3390/app122010443
Hossain SEma SSohn H(2022)Rule-Based Classification Based on Ant Colony OptimizationApplied Computational Intelligence and Soft Computing10.1155/2022/22320002022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2232000
Show More Cited By

Index Terms

A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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Information & Contributors

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 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 the author(s) 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

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Publication History

Published: 20 July 2016

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GECCO '16

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GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Cited By

Kuo RWu CKuo T(2024)An ensemble method with a hybrid of genetic algorithm and K-prototypes algorithm for mixed data classificationComputers & Industrial Engineering10.1016/j.cie.2024.110066190(110066)Online publication date: Apr-2024
https://doi.org/10.1016/j.cie.2024.110066
Ou GHe YFournier-Viger PHuang J(2022)A Novel Mixed-Attribute Fusion-Based Naive Bayesian ClassifierApplied Sciences10.3390/app12201044312:20(10443)Online publication date: 17-Oct-2022
https://doi.org/10.3390/app122010443
Hossain SEma SSohn H(2022)Rule-Based Classification Based on Ant Colony OptimizationApplied Computational Intelligence and Soft Computing10.1155/2022/22320002022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2232000
He YOu GFournier-Viger PHuang JSuganthan P(2022)A novel dependency-oriented mixed-attribute data classification methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116782199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116782
Helal AOtero F(2022)Data stream classification with ant colony optimisationInternational Journal of Intelligent Systems10.1002/int.22809Online publication date: 10-Jan-2022
https://doi.org/10.1002/int.22809
Jyoti BSharma A(2020)Mining Comprehensible Classification Rules with Metaheuristic Strategy: AntMiner2020 7th International Conference on Computing for Sustainable Global Development (INDIACom)10.23919/INDIACom49435.2020.9083683(77-82)Online publication date: Mar-2020
https://doi.org/10.23919/INDIACom49435.2020.9083683
Ayub UIkram AShahzad WAuger AStützle T(2019)AMclrProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321833(4-12)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321833
Jyoti BSharma A(2019)A Metaheuristic Approach for Classification Rule Discovery: AntMiner2019 Fifth International Conference on Image Information Processing (ICIIP)10.1109/ICIIP47207.2019.8985770(119-124)Online publication date: Nov-2019
https://doi.org/10.1109/ICIIP47207.2019.8985770
Helal ABrookhouse JOtero F(2018)Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477643(1-7)Online publication date: Jul-2018
https://doi.org/10.1109/CEC.2018.8477643
Helal AOtero FBosman P(2017)Automatic design of ant-miner mixed attributes for classification rule discoveryProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071306(433-440)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071306

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