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.
@article{urn:eudml:doc:39292,
title = {Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.},
journal = {Mathware and Soft Computing},
volume = {9},
year = {2002},
pages = {251-268},
zbl = {1036.68108},
mrnumber = {MR1983795},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39292}
}
Campos, Luis M. de; Gámez, José A.; Puerta, José M. Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.. Mathware and Soft Computing, Tome 9 (2002) pp. 251-268. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39292/