IEEE Computational Intelligence Magazine - August 2020 - 28

Maria A. Franco, Natalio Krasnogor and Jaume Bacardit
The Interdisciplinary Computing and Complex BioSystems (ICOS)
research group, School of Computing, Newcastle University, UK

Abstract-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

Digital Object Identifier 10.1109/MCI.2020.2998232
Date of current version: 15 July 2020

28

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2020

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.

Corresponding Author: Jaume Bacardit (jaume.bacardit@newcastle.ac.uk)

1556-603X/20©2020IEEE

©ISTOCKPHOTO.COM/MF3D

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



http://www.ISTOCKPHOTO.COM/MF3D

IEEE Computational Intelligence Magazine - August 2020

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