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.
|Title of host publication||GECCO 2021 Companion|
|Subtitle of host publication||Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - Jul 2021|
|Event||2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France|
Duration: 10 Jul 2021 → 14 Jul 2021
|Conference||2021 Genetic and Evolutionary Computation Conference, GECCO 2021|
|Period||10/07/21 → 14/07/21|
Bibliographical notePublisher Copyright:
© 2021 Owner/Author.
Copyright 2021 Elsevier B.V., All rights reserved.
- binary classification
- class binarization
- error correcting output codes
- multiclass classification