Ontology-Enhanced Automated Machine Learning
Date
2024-11-20
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Abstract
This 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.
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Keywords
Automated Machine Learning, Ontology, Machine Learning, Ontologies
Citation
Davies, C. (2024). Ontology-enhanced automated machine learning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.