research-article

Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery

Authors:

James Brookhouse,

Fernando E.B. OteroAuthors Info & Claims

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

Pages 437 - 444

https://doi.org/10.1145/2908812.2908896

Published: 20 July 2016 Publication History

Abstract

Many data mining algorithms do not make use of existing domain knowledge when constructing their models. This can lead to model rejection as users may not trust models that behave contrary to their expectations. Semantic constraints provide a way to encapsulate this knowledge which can then be used to guide the construction of models. One of the most studied semantic constraints in the literature is monotonicity, however current monotonically-aware algorithms have focused on ordinal classification problems. This paper proposes an extension to an ACO-based regression algorithm in order to extract a list of monotonic regression rules. We compared the proposed algorithm against a greedy regression rule induction algorithm that preserves monotonic constraints and the well-known M5' Rules. Our experiments using eight publicly available data sets show that the proposed algorithm successfully creates monotonic rules while maintaining predictive accuracy.

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Cited By

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
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

Index Terms

Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery
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 regression
    2. Machine learning approaches
      1. Rule learning

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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.

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Published: 20 July 2016

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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

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
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

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