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An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results

Xavier-Junior, João C., Freitas, Alex A., Ludermir, Teresa B., Feitosa-Neto, Antonio, Barreto, Cephas A.S. (2019) An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results. Theoretical Computer Science, 805 . pp. 1-18. ISSN 0304-3975. (doi:10.1016/j.tcs.2019.12.002) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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https://doi.org/10.1016/j.tcs.2019.12.002

Abstract

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure.

Item Type: Article
DOI/Identification number: 10.1016/j.tcs.2019.12.002
Uncontrolled keywords: evolutionary algorithms, data mining, machine learning, classification, ensembles, Computational Intelligence Group
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 17 Dec 2019 16:55 UTC
Last Modified: 04 Feb 2020 14:22 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79330 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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