Publicação
A Structured Framework for AutoML: Integrating LLMs through Comparative Experiments
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Santos, Vítor Manuel Pereira Duarte dos | |
| dc.contributor.author | Hassan, Yousef Adel | |
| dc.date.accessioned | 2024-10-25T12:10:06Z | |
| dc.date.available | 2024-10-25T12:10:06Z | |
| dc.date.issued | 2024-10-21 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | This work explores the potential constructive interaction between Generative AI, specifically ChatGPT-4, and Automated Machine Learning (AutoML) frameworks. The study focuses on leveraging ChatGPT-4's capabilities within the CRISP-DM (Cross-Industry Standard Process for Data Mining) model phases to improve the efficiency and effectiveness of data-driven tasks. Through a series of experiments involving classification, regression and clustering, the research compares the performance of ChatGPT-4 in two settings: a global user perspective with general prompts and a structured approach aligned with the CRISP-DM methodology, providing a comparative benchmark. The findings demonstrate that aligning ChatGPT-4's tasks with the CRISP-DM phases yields better performance and more comprehensive insights than the general prompt approach. The study highlights the importance of prompt engineering in optimizing ChatGPT-4's contributions to AutoML tasks, emphasizing its role in improving data preparation, model selection and the evaluation processes. Additionally, the research underscores the ethical considerations and potential challenges associated with integrating generative AI in AutoML, particularly concerning data quality, bias, and model interpretability. | pt_PT |
| dc.identifier.tid | 203787471 | |
| dc.identifier.uri | http://hdl.handle.net/10362/174042 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Generative AI | pt_PT |
| dc.subject | AutoML | pt_PT |
| dc.subject | Prompt engineering | pt_PT |
| dc.subject | ChatGPT | pt_PT |
| dc.subject | Machine learning techniques | pt_PT |
| dc.subject | CRISP-DM | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.title | A Structured Framework for AutoML: Integrating LLMs through Comparative Experiments | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio | pt_PT |