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Lookup NU author(s): Dr Maria Franco Gaviria,
Professor Natalio KrasnogorORCiD,
Dr Jaume Bacardit
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.
For re-use rights please refer to the publisher's terms and conditions.
© 2005-2012 IEEE. The success of any machine learning technique depends on the correct setting of its parameters and, when it comes to large-scale datasets, hand-tuning these parameters becomes impractical. However, very large-datasets can be pre-processed in order to distil information that could help in appropriately setting various systems parameters. In turn, this makes sophisticated machine learning methods easier to use to end-users. Thus, by modelling the performance of machine learning algorithms as a function of the structure inherent in very large datasets one could, in principle, detect "hotspots"in the parameters' space and thus, auto-tune machine learning algorithms for better dataset-specific performance. In this work we present a parameter setting mechanism for a rule-based evolutionary machine learning system that is capable of finding the adequate parameter value for a wide variety of synthetic classification problems with binary attributes and with/without added noise. Moreover, in the final validation stage our automated mechanism is able to reduce the computational time of preliminary experiments up to 71% for a challenging real-world bioinformatics dataset.
Author(s): Franco MA, Krasnogor N, Bacardit J
Publication type: Article
Publication status: Published
Journal: IEEE Computational Intelligence Magazine
Print publication date: 01/08/2020
Online publication date: 15/07/2020
Acceptance date: 02/04/2018
Date deposited: 06/11/2020
ISSN (print): 1556-603X
ISSN (electronic): 1556-6048
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