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seeAlso	HTML Summary of #82232 A robust experimental evaluation of automated multi-label classification methods
sameAs	A robust experimental evaluation of automated multi-label classification methods
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dc:hasVersion	A robust experimental evaluation of automated multi-label classification methods (PDF)
Title	A robust experimental evaluation of automated multi-label classification methods
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2020-06-01
Creator	Gisele L. Pappa Cristiano G. Pimenta A. Freitas Alex G. C. de Sá
status	peer reviewed published
Publisher	ACM
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.
Is Part Of	Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO’20) https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/82232#authors
presented at	2020 Genetic and Evolutionary Computation Conference (GECCO’20),
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #82232 A robust experimental evaluation of automated multi-label classification methods

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