Abstract
Bayesian networks are a useful tool in the representation
of uncertain knowledge. This paper
proposes a new algorithm to learn the structure
of a Bayesian network. It does this by conducting
a search through the space of equivalence
classes of Bayesian networks using Ant
Colony Optimization (ACO). To this end, two
novel extensions of traditional ACO techniques
are proposed and implemented. Firstly, multiple
types of moves are allowed on the ACO
construction graph. Secondly, moves can be
given in terms of arbitrary identifiers. The algorithm
is implemented and tested. The results
show that ACO performs better than a greedy
search whilst searching in the space of equivalence
classes.
Original language | English |
---|---|
Pages | 111-118 |
Number of pages | 8 |
Publication status | Published - 2006 |