Ontology-Enhanced Automated Machine Learning

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dc.contributor.advisorDenzinger, Jorg
dc.contributor.advisorMaurer, Frank
dc.contributor.authorDavies, Cooper T.S.
dc.contributor.committeememberJacob, Christian
dc.contributor.committeememberWalker, Robert
dc.contributor.committeememberDick, Scott
dc.contributor.committeememberBoyd, Jeffrey
dc.date2025-02
dc.date.accessioned2024-11-21T22:42:11Z
dc.date.available2024-11-21T22:42:11Z
dc.date.issued2024-11-20
dc.description.abstractThis thesis addresses the challenge of bridging the gap between traditional Problem-Specific Machine Learning (PSML) and Automated Machine Learning (AutoML) systems. While PSML offers high accuracy but demands substantial expertise, AutoML aims to auto-mate the process of building a machine learning (ML) model but often lacks domain-specific knowledge. To address this, we propose Ontology-Enhanced AutoML, a novel approach that integrates domain knowledge from ontologies into the AutoML pipeline. We first examine the current landscape of AutoML, highlighting the complexities faced by a system in selecting appropriate algorithms and hyperparameters. We identify the limitations of existing AutoML systems, particularly their blind reliance on datasets, which often leads to poor performance and lengthy training times. Our thesis presents experiments demonstrating the effectiveness of Ontology-Enhanced AutoML in mitigating these challenges. By incorporating mechanisms for ontology-based feature extraction and example filtering, we demonstrate significant improvements in accu-racy and optimization time compared to traditional AutoML. These results highlight the potential of Ontology-Enhanced AutoML to provide a wide range of systems lying between the extremes of PSML and AutoML. This thesis contributes not only a technical solution but also a conceptual framework for understanding ML as a spectrum. We discuss implications for future research and the potential for further advancements in bridging the gap between domain expertise and ML proficiency.
dc.identifier.citationDavies, C. (2024). Ontology-enhanced automated machine learning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/120089
dc.language.isoen
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectAutomated Machine Learning
dc.subjectOntology
dc.subjectMachine Learning
dc.subjectOntologies
dc.subject.classificationArtificial Intelligence
dc.subject.classificationComputer Science
dc.titleOntology-Enhanced Automated Machine Learning
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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