DocumentCode
2335758
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
fYear
2001
fDate
2001
Firstpage
615
Lastpage
616
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-7695-1119-8
Type
conf
DOI
10.1109/ICDM.2001.989580
Filename
989580
Link To Document