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Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification

Lookup NU author(s): Dr Maria Franco Gaviria, Professor Natalio Krasnogor, Dr Jaume Bacardit

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

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Abstract

© 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.


Publication metadata

Author(s): Franco MA, Krasnogor N, Bacardit J

Publication type: Article

Publication status: Published

Journal: IEEE Computational Intelligence Magazine

Year: 2020

Volume: 15

Issue: 3

Pages: 28-46

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

Publisher: IEEE

URL: https://doi.org/10.1109/MCI.2020.2998232

DOI: 10.1109/MCI.2020.2998232


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