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Learning bayesian networks by ant colony optimisation: searching in two different spaces
dc.contributor.author | Campos, Luís Miguel de |
dc.contributor.author | Gámez Martín, José Antonio |
dc.contributor.author | Puerta Castellón, José Miguel |
dc.date.accessioned | 2007-10-02T12:18:18Z |
dc.date.available | 2007-10-02T12:18:18Z |
dc.date.issued | 2002 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3629 |
dc.description.abstract | The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used. A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied to solve a great variety of problems, being remarkable the performance achieved in those problems related to path (permutation) searching in graphs, such as the Traveling Salesman Problem. In two previous works [13,12], the authors have approached the problem of learning Bayesian networks by means of the search+score methodology using ACO as the search engine. As in these articles the search was performed in different search spaces, in the space of orderings [13] and in the space of directed acyclic graphs [12]. In this paper we compare both approaches by analysing the results obtained and the differences in the design and implementation of both algorithms. |
dc.format.extent | 251-268 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 2002 Vol. 9 Núm. 2 [ -3 ] |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | Ant Colony Optimization (ACO) |
dc.subject.other | Bayesian networks |
dc.title | Learning bayesian networks by ant colony optimisation: searching in two different spaces |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Estadística bayesiana |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |