Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes
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 no...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1591878 |
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| author | Stefano Beretta Mauro Castelli Ivo Gonçalves Roberto Henriques Daniele Ramazzotti |
| author_facet | Stefano Beretta Mauro Castelli Ivo Gonçalves Roberto Henriques Daniele Ramazzotti |
| author_sort | Stefano Beretta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-905c74e00f82497aa26fa83e3d5baf6f |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-905c74e00f82497aa26fa83e3d5baf6f2025-08-20T03:20:31ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/15918781591878Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic SchemesStefano Beretta0Mauro Castelli1Ivo Gonçalves2Roberto Henriques3Daniele Ramazzotti4DISCo, Universitá degli Studi di Milano-Bicocca, 20126 Milano, ItalyNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalINESC Coimbra, DEEC, University of Coimbra, Coimbra, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalDepartment of Pathology, Stanford University, Stanford, California, USAOne 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.http://dx.doi.org/10.1155/2018/1591878 |
| spellingShingle | Stefano Beretta Mauro Castelli Ivo Gonçalves Roberto Henriques Daniele Ramazzotti Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes Complexity |
| title | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes |
| title_full | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes |
| title_fullStr | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes |
| title_full_unstemmed | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes |
| title_short | Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes |
| title_sort | learning the structure of bayesian networks a quantitative assessment of the effect of different algorithmic schemes |
| url | http://dx.doi.org/10.1155/2018/1591878 |
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