Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Título inglés	Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.
Título español	Aprendizaje de redes bayesianas mediante optimización basada en colonias de hormigas: búsqueda en dos espacios diferentes.
Autor/es	Campos, Luis M. de ; Gámez, José A. ; Puerta, José M.
Organización	Dep. Cienc. Comput. Intel. Artif. Univ. Granada, Granada, España;Dep. Informát. Univ. Castilla-La Mancha, Albacete, España
Revista	1134-5632
Publicación	2002, 9 (2-3): 251-268, 33 Ref.
Tipo de documento	articulo
Idioma	Inglés
Resumen inglés	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.
Clasificación UNESCO	120304 ; 120700
Palabras clave español	Análisis bayesiano ; Algoritmo de búsqueda ; Problemas combinatorios ; Optimización global
Código MathReviews	MR1983795
Código Z-Math	Zbl 1036.68108
Acceso al artículo completo