Title :
Evolutionary structure learning algorithm for Bayesian network and Penalized Mutual Information metric
Author :
Li, Gang ; Tong, FU ; Dai, Honghua
Author_Institution :
Sch. of Comput. & Math., Deakin Univ., Vic., Australia
Abstract :
The paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and an evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising
Keywords :
belief networks; data analysis; evolutionary computation; learning (artificial intelligence); probability; search problems; Bayesian network structure learning; Penalized Mutual Information metric; evolutionary algorithm; evolutionary structure learning algorithm; probability distribution; structure search; uncertainty; Algorithm design and analysis; Bayesian methods; Computer networks; Databases; Distributed computing; Evolutionary computation; Genetic mutations; Mathematics; Mutual information; Probability distribution;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989580
