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Computational intelligent systems : evolving dynamic Bayesian networks
Includes abstract.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2014
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| _version_ | 1867613240063688704 |
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| access_status_str | Open Access |
| author | Osunmakinde, Isaac Olusegun |
| author2 | Bagula, Antoine |
| author_browse | Bagula, Antoine Osunmakinde, Isaac Olusegun |
| author_facet | Bagula, Antoine Osunmakinde, Isaac Olusegun |
| author_sort | Osunmakinde, Isaac Olusegun |
| collection | Thesis |
| description | Includes abstract. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/6429 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:58.612Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/6429 Computational intelligent systems : evolving dynamic Bayesian networks Osunmakinde, Isaac Olusegun Bagula, Antoine Computer Science Includes abstract. Includes bibliographical references (p. 163-172). In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are ideally suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the near future. The use of Dynamic Bayesian Networks in achieving situation awareness has been poorly explored in current research efforts. This research completely evolves DBNs automatically from any environment captured as multivariate time series (MTS) which minimizes the approximations and mitigates the challenges of choice of models. This potentially accommodates both highly skilled users and non-expert practitioners, and attracts diverse real-world application areas for DBNs. The architecture of our EDBN uses a combined strategy as it resolves two orthogonal issues to address the challenging problems: (1) evolving DBNs in the absence of domain experts and (2) mitigating computational intensity (or NP-hard) problems with economic scalability. Most notably, the major contributions of this thesis are as follows: the development of a new class of temporal probabilistic modeling (EDBN), whose architecture facilitates the demonstration of its emergent situation awareness (ESA) and emergent future situation awareness (EFSA) technologies. The ESA and its variant reveal hidden patterns over current and future time steps respectively. Among other contributions are the development and integration of an economic scalable framework called dynamic memory management in adaptive learning (DMMAL) into the architecture of the EDBN to emerge such network models from environments captured as massive datasets; the design of configurable agent actuators; adaptive operators; representative partitioning algorithms which facilitate the scalability framework; formal development and optimization of genetic algorithm (GA) to emerge optimal Bayesian networks from datasets, with emphasis on backtracking avoidance; and diverse applications of EDBN technologies such as business intelligence, revealing trends of insulin dose to medical patients, water quality management, project profitability analysis, sensor networks, etc. 2014-08-13T19:31:51Z 2014-08-13T19:31:51Z 2009 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/6429 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Osunmakinde, Isaac Olusegun Computational intelligent systems : evolving dynamic Bayesian networks |
| thesis_degree_str | Doctoral |
| title | Computational intelligent systems : evolving dynamic Bayesian networks |
| title_full | Computational intelligent systems : evolving dynamic Bayesian networks |
| title_fullStr | Computational intelligent systems : evolving dynamic Bayesian networks |
| title_full_unstemmed | Computational intelligent systems : evolving dynamic Bayesian networks |
| title_short | Computational intelligent systems : evolving dynamic Bayesian networks |
| title_sort | computational intelligent systems evolving dynamic bayesian networks |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/6429 |
| work_keys_str_mv | AT osunmakindeisaacolusegun computationalintelligentsystemsevolvingdynamicbayesiannetworks |