Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach
Bayesian network has gained increasing popularity among the data scientists and research communities, because of its inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian learning, with continuous and categorical variable...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/159579 |
| _version_ | 1811694709928624128 |
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| author | Das, Monidipa Ghosh, Soumya K. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Das, Monidipa Ghosh, Soumya K. |
| author_sort | Das, Monidipa |
| collection | NTU |
| description | Bayesian network has gained increasing popularity
among the data scientists and research communities, because of its
inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian
learning, with continuous and categorical variables, often shows
poor performance because of parameter value uncertainty, arising
due to strict boundary value of the discretized data and lack of
knowledge on domain semantics. In this work, we propose semFBnet, a variant of Bayesian network with incorporated fuzziness
and semantic knowledge, to reduce the uncertainty during parameter learning. The performance of semFBnet has been validated
with prediction of daily meteorological conditions in two states
of India, namely West Bengal and Delhi, for the years 2015 and
2016, respectively. The study of Dawid-Sebastiani score and the
confidence interval analysis, in comparison with the state-of-theart and benchmark prediction techniques, demonstrate the effectiveness of the proposed semFBnet in reducing parameter value
uncertainty. |
| first_indexed | 2024-10-01T07:11:54Z |
| format | Journal Article |
| id | ntu-10356/159579 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:11:54Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1595792022-06-28T01:06:12Z Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach Das, Monidipa Ghosh, Soumya K. School of Computer Science and Engineering Engineering::Computer science and engineering Bayesian Network Parameter Learning Bayesian network has gained increasing popularity among the data scientists and research communities, because of its inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian learning, with continuous and categorical variables, often shows poor performance because of parameter value uncertainty, arising due to strict boundary value of the discretized data and lack of knowledge on domain semantics. In this work, we propose semFBnet, a variant of Bayesian network with incorporated fuzziness and semantic knowledge, to reduce the uncertainty during parameter learning. The performance of semFBnet has been validated with prediction of daily meteorological conditions in two states of India, namely West Bengal and Delhi, for the years 2015 and 2016, respectively. The study of Dawid-Sebastiani score and the confidence interval analysis, in comparison with the state-of-theart and benchmark prediction techniques, demonstrate the effectiveness of the proposed semFBnet in reducing parameter value uncertainty. 2022-06-28T01:06:12Z 2022-06-28T01:06:12Z 2019 Journal Article Das, M. & Ghosh, S. K. (2019). Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach. IEEE Transactions On Emerging Topics in Computational Intelligence, 5(3), 361-372. https://dx.doi.org/10.1109/TETCI.2019.2939582 2471-285X https://hdl.handle.net/10356/159579 10.1109/TETCI.2019.2939582 3 5 361 372 en IEEE Transactions on Emerging Topics in Computational Intelligence © 2019 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Bayesian Network Parameter Learning Das, Monidipa Ghosh, Soumya K. Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title | Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title_full | Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title_fullStr | Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title_full_unstemmed | Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title_short | Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach |
| title_sort | reducing parameter value uncertainty in discrete bayesian network learning a semantic fuzzy bayesian approach |
| topic | Engineering::Computer science and engineering Bayesian Network Parameter Learning |
| url | https://hdl.handle.net/10356/159579 |
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