TY - JOUR
T1 - Learning the structure of Bayesian networks
T2 - A quantitative assessment of the effect of different algorithmic schemes
AU - Beretta, Stefano
AU - Castelli, Mauro
AU - Gonçalves, Ivo
AU - Henriques, Roberto
AU - Ramazzotti, Daniele
N1 - Beretta, S., Castelli, M., Gonçalves, I., Henriques, R., & Ramazzotti, D. (2018). Learning the structure of Bayesian networks: A quantitative assessment of the effect of different algorithmic schemes. Complexity, 2018, [1591878]. DOI: 10.1155/2018/1591878
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059264626&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000445545600001
U2 - 10.1155/2018/1591878
DO - 10.1155/2018/1591878
M3 - Article
AN - SCOPUS:85059264626
VL - 2018
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 1591878
ER -