Title: A Study on the Evolution of Bayesian Network Graph Structures

Authors: Jorge Muruzábal, Carlos Cotta

Level:  Advanced

Abstract:

Bayesian Networks (BN) are often sought as useful descriptive and predictive models for the available data. Learning algorithms trying to ascertain automatically the best BN model (graph structure) for some input data are of the greatest interest for practical reasons. In this paper we examine a number of evolutionary programming algorithms for this network induction problem. Our algorithms build on recent advances in the field and are based on selection and various kinds of mutation operators (working at both the directed acyclic and essential graph level). A review of related evolutionary work is also provided. We analyze and discuss the merit and computational toll of these EP algorithms in a couple of benchmark tasks. Some general conclusions about the most efficient algorithms, and the most appropriate search landscapes are presented.

Categories: Articles

Langages: English

Files: *.pdf