article

Improving the interpretability of classification rules discovered by an ant colony algorithm: Extended results

Authors:

Fernando E. B. Otero,

Alex A. FreitasAuthors Info & Claims

Evolutionary Computation, Volume 24, Issue 3

Pages 385 - 409

https://doi.org/10.1162/EVCO_a_00155

Published: 01 September 2016 Publication History

Abstract

Most ant colony optimization ACO algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner<inline-formula><inline-graphic xlink="EVCO_a_00155inline1.gif" xlink:type="simple"/></inline-formula> algorithm, where an ACO-based procedure is used to create a complete list of rules ordered rules, i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner<inline-formula><inline-graphic xlink="EVCO_a_00155inline2.gif" xlink:type="simple"/></inline-formula> algorithm to discover a set of rules unordered rules. The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner<inline-formula><inline-graphic xlink="EVCO_a_00155inline3.gif" xlink:type="simple"/></inline-formula> producing ordered rules are also presented.

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Improving the interpretability of classification rules discovered by an ant colony algorithm: Extended results
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image Evolutionary Computation

Evolutionary Computation Volume 24, Issue 3

Fall 2016

185 pages

ISSN:1063-6560

EISSN:1530-9304

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Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 September 2016

Published in EVOL Volume 24, Issue 3

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Cited By

Vilone GLongo L(2022)Notions of explainability and evaluation approaches for explainable artificial intelligenceInformation Fusion10.1016/j.inffus.2021.05.00976:C(89-106)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.inffus.2021.05.009
Huo ZZhu WPei P(2021)Network Traffic Statistics Method for Resource-Constrained Industrial Project Group Scheduling under Big DataWireless Communications & Mobile Computing10.1155/2021/55946632021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5594663
Manzali YChahhou MMohajir M(2021)Classification Algorithm Using Branches ImportanceNeural Processing Letters10.1007/s11063-021-10664-x54:2(891-907)Online publication date: 5-Nov-2021
https://dl.acm.org/doi/10.1007/s11063-021-10664-x
Zhao LShang ZQin AZhang TZhao LWei YTang Y(2019)A cost-sensitive meta-learning classifierFuture Generation Computer Systems10.1016/j.future.2019.05.080100:C(1031-1043)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1016/j.future.2019.05.080
Otero F(2017)MYRAProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082471(1247-1254)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082471
Vandromme MJacques JTaillard JHansske AJourdan LDhaenens C(2017)Extraction and optimization of classification rules for temporal sequencesKnowledge-Based Systems10.1016/j.knosys.2017.02.001122:C(148-158)Online publication date: 15-Apr-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.02.001

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