Learning Essential Graph with Immune Co-Evolutionary Algorithm
Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number of illegal structures is exponential, which infect the efficiency of structure learning; (2) comparing the structures in same equivalent class slow down the speed of convergence;(3) if the prior distribution for each structure is equal, the more structures contain in the equivalent class the higher prior probability of the class has. This paper employs two competitive bio-inspired algorithms, immune algorithm and co-evolutionary algorithm, for learning Essential graph. The algorithm combines dependency analysis and search-scoring approach together. Experiments show that the searching space was decreased, compare with prior works, the convergence speed and the efficiency was improved.
Bayesian network structure learining co-evolutionary algorithm artificial immune system
Haiyang Jia Juan Chen Dayou Liu
College of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin China 130012
国际会议
西安
英文
853-856
2010-08-07（万方平台首次上网日期，不代表论文的发表时间）