A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service

Acid, Silvia; Campos, Luis M. de; Fernández‐Luna, Juan M.; Ochoa-Rodríguez, Susana; Rodríguez, José María Rodríguez; Salcedo, José Luis Villarroel

doi:10.1016/j.artmed.2003.11.002

Cited by 73 publications

(43 citation statements)

References 16 publications

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“…A BN represents joint probability distributions Relationships between variables based on the theory of BN (Neapolitan, 2004), represented by arcs in the graph, could represent causality, relevance or relations of direct dependence between variables. However, for the purpose of this research we do not assume a causal interpretation of the arcs in the networks such as in Acid et al (2004). Consequently, the arcs are interpreted as direct dependence relationships between the linked variables, and the absence of arcs means the absence of direct dependence between variables; however, indirect dependence relationships between variables could exist.…”

Section: Bayesian Networkmentioning

confidence: 99%

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Oña

López

Mujalli

et al. 2013

Accident Analysis & Prevention

219

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One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3,229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BN) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analyzed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.

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Section: Bayesian Networkmentioning

confidence: 99%

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Oña

López

Mujalli

et al. 2013

Accident Analysis & Prevention

219

View full text Add to dashboard Cite

show abstract

“…, x n }, n≥1 be a set of variables. A BN over a set of variables U is a network structure, which is a Directed Acyclic Graph (DAG) over U and a set of probability tables B p = {p(x i |pa(x i ), x i U)} where is the set of parents or antecedents of x i in BN and i= (1,2,3…”

Section: Bayesian Network Definitionmentioning

confidence: 99%

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Mujalli

Oña

2011

Journal of Safety Research

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“…In addition, the theory of learning Bayesian networks has deep connections with variable selection for classification and has been used to design algorithms that optimally solve the problem under certain conditions Tsamardinos, Aliferis & Statnikov, 2003c). Finally, Bayesian network learning has been used in information retrieval (Baeze-Yates & Ribiero-Neto, 1999), natural language processing (Chapman et al, 2001), and for the analysis of a medical service's performance for management decisions (Acid et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

The max-min hill-climbing Bayesian network structure learning algorithm

2006

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We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc paper/mmhc index.html.

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A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service

Cited by 73 publications

References 16 publications

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

The max-min hill-climbing Bayesian network structure learning algorithm

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