The FEDHC Bayesian Network Learning Algorithm
The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software <i>R</i>...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/15/2604 |
| _version_ | 1797413121630928896 |
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| author | Michail Tsagris |
| author_facet | Michail Tsagris |
| author_sort | Michail Tsagris |
| collection | DOAJ |
| description | The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software <i>R</i> is prohibitively expensive, and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, which can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show that it is computationally efficient, and that it produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software <i>R</i>. |
| first_indexed | 2024-03-09T05:12:59Z |
| format | Article |
| id | doaj.art-6015504beb704a21bc61d627ecda88ae |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T05:12:59Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6015504beb704a21bc61d627ecda88ae2023-12-03T12:47:26ZengMDPI AGMathematics2227-73902022-07-011015260410.3390/math10152604The FEDHC Bayesian Network Learning AlgorithmMichail Tsagris0Department of Economics, University of Crete, Gallos Campus, 74100 Rethymnon, GreeceThe paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software <i>R</i> is prohibitively expensive, and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, which can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show that it is computationally efficient, and that it produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software <i>R</i>.https://www.mdpi.com/2227-7390/10/15/2604causalityBayesian networksscalability |
| spellingShingle | Michail Tsagris The FEDHC Bayesian Network Learning Algorithm Mathematics causality Bayesian networks scalability |
| title | The FEDHC Bayesian Network Learning Algorithm |
| title_full | The FEDHC Bayesian Network Learning Algorithm |
| title_fullStr | The FEDHC Bayesian Network Learning Algorithm |
| title_full_unstemmed | The FEDHC Bayesian Network Learning Algorithm |
| title_short | The FEDHC Bayesian Network Learning Algorithm |
| title_sort | fedhc bayesian network learning algorithm |
| topic | causality Bayesian networks scalability |
| url | https://www.mdpi.com/2227-7390/10/15/2604 |
| work_keys_str_mv | AT michailtsagris thefedhcbayesiannetworklearningalgorithm AT michailtsagris fedhcbayesiannetworklearningalgorithm |