A NEAT-based multiclass classification method with class binarization

Zhenyu Gao, Gongjin Lan

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Multiclass classification is a fundamental and challenging task in machine learning. Class binarization is a popular method to achieve multiclass classification by converting multiclass classification to multiple binary classifications. NeuroEvolution, such as NeuroEvolution of Augmenting Topologies (NEAT), is broadly used to generate Artificial Neural Networks by applying evolutionary algorithms. In this paper, we propose a new method, ECOC-NEAT, which applies Error-Correcting Output Codes (ECOC) to improve the multiclass classification of NEAT. The experimental results illustrate that ECOC-NEAT with a considerable number of binary classifiers is highly likely to perform well. ECOC-NEAT also shows significant benefits in a flexible number of binary classifiers and strong robustness against errors.

Original languageEnglish
Title of host publicationGECCO 2021 Companion
Subtitle of host publicationProceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Number of pages2
ISBN (Electronic)9781450383516
Publication statusPublished - Jul 2021
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 10 Jul 202114 Jul 2021


Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

Copyright 2021 Elsevier B.V., All rights reserved.


  • binary classification
  • class binarization
  • error correcting output codes
  • multiclass classification
  • NEAT
  • one-vs-all
  • one-vs-one


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