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Explainable Bayesian networks : taxonomy, properties and approximation methods
Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2024
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| _version_ | 1867613714412208128 |
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| access_status_str | Open Access |
| author2 | De Waal, Alta |
| author_browse | De Waal, Alta |
| author_facet | De Waal, Alta |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/97333 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:31.851Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/97333 Explainable Bayesian networks : taxonomy, properties and approximation methods De Waal, Alta inekederks1@gmail.com Derks, Iena Petronella UCTD Sustainable Development Goals (SDGs) Explainable artificial intelligence Bayesian networks Post-hoc explanation Same-decision probability Most relevant explanation Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2024. Technological advances have integrated artificial intelligence (AI) into various scientific fields, necessitating understanding AI-derived decisions. The field of explainable artificial intelligence (XAI) has emerged to address transparency concerns, offering both transparent models and post-hoc explanation techniques. Recent research emphasises the importance of developing transparent models, with a focus on enhancing the interpretability of these models. An example of a transparent model that would benefit from enhanced post-hoc explainability is Bayesian networks. This research investigates the current state of explainability in Bayesian networks. Literature includes three categories of explanation: explanation of the model, reasoning, and evidence. Drawing upon these categories, we formulate a taxonomy of explainable Bayesian networks. Following this, we extend the taxonomy to include explanation of decisions, an area recognised as neglected within the broader XAI research field. This includes using the same-decision probability, a threshold-based confidence measure, as a stopping and selection criteria for decision-making. Additionally, acknowledging computational efficiency as a concern in XAI, we introduce an approximate forward-gLasso algorithm as a solution for efficiently solving the most relevant explanation. We compare the proposed algorithm with a local, exhaustive forward search. The forward-gLasso algorithm demonstrates accuracy comparable to the forward search while reducing the average neighbourhood size, leading to computationally efficient explanations. All coding was done in R, building on existing packages for Bayesian networks. As a result, we develop an open-source R package capable of generating explanations of evidence for Bayesian networks. Lastly, we demonstrate the practical insights gained from applying post-hoc explanations on real-world data, such as the South African Victims of Crime Survey 2016 - 2017. Statistics PhD (Mathematical Statistics) Unrestricted Faculty of Economic And Management Sciences 2024-07-30T13:11:52Z 2024-07-30T13:11:52Z 2024-09-03 2024-07-22 Thesis * S2024 http://hdl.handle.net/2263/97333 DOI: https://doi.org/10.25403/UPresearchdata.26403883.v1 10.25403/UPresearchdata.26403883 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Explainable artificial intelligence Bayesian networks Post-hoc explanation Same-decision probability Most relevant explanation Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title | Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title_full | Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title_fullStr | Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title_full_unstemmed | Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title_short | Explainable Bayesian networks : taxonomy, properties and approximation methods |
| title_sort | explainable bayesian networks taxonomy properties and approximation methods |
| topic | UCTD Sustainable Development Goals (SDGs) Explainable artificial intelligence Bayesian networks Post-hoc explanation Same-decision probability Most relevant explanation |
| url | http://hdl.handle.net/2263/97333 |