Learning Bayesian network structure with immune algorithm

Abstract

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This paper proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Furthermore, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.