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DC Field | Value | Language |
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dc.date.accessioned | 2019-02-22T12:07:42Z | - |
dc.date.available | 2019-02-22T12:07:42Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Sciortino, J. (2018). A Swarm intelligence based approach to optimisation of machine learning problems using a parallelised framework and the cloud (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/40347 | - |
dc.description | B.SC.SOFTWARE DEVELOPMENT | en_GB |
dc.description.abstract | In the recent years, the field of machine learning (ML) has become popular and has been utilised to achieve a wide variety of advances in many different areas. However, the information processing capability of certain ML models is dependent on a number of user-defined hyperparameters which need fine tuning for optimal performance. Due to the large sizes of the search spaces and datasets involved, algorithm complexity, long execution times and the high cost of processing, the use of brute force searching is not always a feasible approach. In these cases, finding a near-optimal solution given short time budgets is acceptable.The study describes an approach, based on ant swarm intelligence optimisation, for dealing with these types of combinatorial problems. A parallelisation approach was chosen and applied to the optimisation algorithms with the aim of identifying satisfactory performing ML model parameter configurations in a shorter time period. Finally, an investigation of the benefits and limitations of using cloud virtual machines for the experimentation phases of projects of this type was done during this study. The method has been tested on four classification problems having different sizes and features. The results show that the evolved ML models achieved satisfactory accuracy and generalisation ability while the optimiser applications were able to identify better performing parameter sets during training phase at earlier stages when compared to a purely random search in some cases. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Swarm intelligence | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.subject | Genetic algorithms | en_GB |
dc.title | A Swarm intelligence based approach to optimisation of machine learning problems using a parallelised framework and the cloud | en_GB |
dc.type | bachelorThesis | 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.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Computer Information Systems | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Sciortino, Jerome | - |
Appears in Collections: | Dissertations - FacICT - 2018 Dissertations - FacICTCIS - 2018 |
Files in This Item:
File | Description | Size | Format | |
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18BSCITSD26.pdf Restricted Access | 2.13 MB | Adobe PDF | View/Open Request a copy |
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