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A robust experimental evaluation of automated multi-label classification methods

de Sá, Alex G. C. and Pimenta, Cristiano G. and Pappa, Gisele L. and Freitas, Alex A. (2020) A robust experimental evaluation of automated multi-label classification methods. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. ACM, New York, USA, pp. 175-183. ISBN 978-1-4503-7128-5. (doi:10.1145/3377930.3390231) (KAR id:82232)

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https://dx.doi.org/10.1145/3377930.3390231

Abstract

Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. In this work, we provide a general comparison of five automated multi-label classification methods - two evolutionary methods, one Bayesian optimization method, one random search and one greedy search - on 14 datasets and three designed search spaces. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKAGGP. Auto-MEKAGGP presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method.

Item Type: Book section
DOI/Identification number: 10.1145/3377930.3390231
Uncontrolled keywords: machine learning, data mining, evolutionary algorithms, multi-label classification, automated machine learning (Auto-ML)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 25 Jul 2020 13:37 UTC
Last Modified: 16 Feb 2021 14:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82232 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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