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https://hdl.handle.net/10316/107537
Título: | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes | Autor: | Beretta, Stefano Castelli, Mauro Gonçalves, Ivo Henriques, Roberto Ramazzotti, Daniele |
Data: | 2018 | Editora: | Hindawi | Projeto: | Regional Operational Programme CENTRO2020 within the scope of the project CENTRO-01-0145-FEDER-000006 | Título da revista, periódico, livro ou evento: | Complexity | Volume: | 2018 | Resumo: | One of the most challenging tasks when adopting Bayesian networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions and by the fact that the problem is NP-hard. Hence, a full enumeration of all the possible solutions is not always feasible and approximations are often required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before. For this reason, in this work, we provide a detailed comparison of many different state-of-the-art methods for structural learning on simulated data considering both BNs with discrete and continuous variables and with different rates of noise in the data. In particular, we investigate the performance of different widespread scores and algorithmic approaches proposed for the inference and the statistical pitfalls within them. | URI: | https://hdl.handle.net/10316/107537 | ISSN: | 1076-2787 1099-0526 |
DOI: | 10.1155/2018/1591878 | Direitos: | openAccess |
Aparece nas coleções: | I&D INESCC - Artigos em Revistas Internacionais |
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Learning-the-structure-of-Bayesian-networks-A-quantitative-assessment-of-the-effect-of-different-algorithmic-schemesComplexity.pdf | 3.21 MB | Adobe PDF | Ver/Abrir |
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