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A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

Xavier-Junior, Joao C., Freitas, Alex A., Feitosa-Neto, Antonino, Ludermir, Teresa B. (2018) A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles. In: IEEE Conference on Intelligent Systems. IEEE Conference on Intelligent Systems. . pp. 462-467. IEEE, USA ISBN 978-1-5386-8023-0. (doi:10.1109/BRACIS.2018.00086) (KAR id:73717)

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Abstract

Automated Machine Learning (Auto-ML) is an emerging area of ML which consists of automatically selecting the best ML algorithm and its best hyper-parameter settings for a given input dataset, by doing a search in a large space of candidate algorithms and settings. In this work we propose a new Evolutionary Algorithm (EA) for the Auto-ML task of automatically selecting the best ensemble of classifiers and their hyper-parameter settings for an input dataset. The proposed EA was compared against a version of the well-known Auto-WEKA method adapted to search in the same space of algorithms and hyper-parameter settings as the EA. In general, the EA obtained significantly smaller classification error rates than that Auto-WEKA version in experiments with 15 classification datasets.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/BRACIS.2018.00086
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Alex Freitas
Date Deposited: 01 May 2019 11:00 UTC
Last Modified: 16 Feb 2021 14:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73717 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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