
Learning Equivalence Classes of Bayesian Networks Structures
Approaches to learning Bayesian networks from data typically combine a s...
read it

Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
We introduce Selective Greedy Equivalence Search (SGES), a restricted ve...
read it

On Local Optima in Learning Bayesian Networks
This paper proposes and evaluates the kgreedy equivalence search algori...
read it

Greedy Causal Discovery is Geometric
Finding a directed acyclic graph (DAG) that best encodes the conditional...
read it

On the Construction of the Inclusion Boundary Neighbourhood for Markov Equivalence Classes of Bayesian Network Structures
The problem of learning Markov equivalence classes of Bayesian network s...
read it

Improved learning of Bayesian networks
The search space of Bayesian Network structures is usually defined as Ac...
read it

ExpertBayes: Automatically refining manually built Bayesian networks
Bayesian network structures are usually built using only the data and st...
read it
Learning Bayesian Network Equivalence Classes with Ant Colony Optimization
Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACOE, 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. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACOE performs better than a greedy search and other stateoftheart and metaheuristic algorithms whilst searching in the space of equivalence classes.
READ FULL TEXT
Comments
There are no comments yet.