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DC Field | Value | Language |
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dc.contributor.author | Camilleri, Michel | - |
dc.contributor.author | Montebello, Matthew | - |
dc.date.accessioned | 2017-12-14T13:31:33Z | - |
dc.date.available | 2017-12-14T13:31:33Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Camilleri, M., & Montebello, M. (2017). Optimising the meta-optimiser in machine learning problems. 9th International Conference on Machine Learning and Computing, ICMLC 2017, Singapore. 15-22. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/24695 | - |
dc.description.abstract | The improvement of machine learning algorithm performance can be achieved via the search across the algorithm’s parameter space by a number of meta-heuristic based optimisation techniques or meta-optimisers. These meta-optimisers vary in their approach and have been shown to perform differently on various optimisation problems. This paper takes the concept of metaoptimisation further and explores the potential of fine tuning the meta-optimiser itself in order to improve the optimisation process. The study is centred around the use of a Simple Genetic Algorithm (SGA) based meta-optimisation approach and the measurement of the effect of changing its mutation and crossover rates on a set of machine learning algorithms and standard datasets. Although the results did not evidence any significant effect on the overall performance of the SGA as a machine learner meta-optimiser they showed significant effects on the SGA's efficiency. This discovery is beneficial for machine learning optimisation problems, particularly those that require high processing costs, have low computational budgets or both. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | ACM | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Computational learning theory | en_GB |
dc.title | Optimising the meta-optimiser in machine learning problems | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 9th International Conference on Machine Learning and Computing, ICMLC 2017 | en_GB |
dc.bibliographicCitation.conferenceplace | Singapore, Singapore, 24-26/02/2017 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1145/3055635.3056613 | - |
Appears in Collections: | Scholarly Works - FacICTAI Scholarly Works - FacICTCIS |
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File | Description | Size | Format | |
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Optimising the Meta-Optimiser in Machine Learning Problems - CamilleriMontebello3.pdf Restricted Access | Full paper | 668.23 kB | Adobe PDF | View/Open Request a copy |
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