Title :
A review of some Bayesian Belief Network structure learning algorithms
Author :
Mittal, Sangeeta ; Maskara, S.L.
Author_Institution :
Dept. of CS&IT, JIIT, Noida, India
Abstract :
Bayesian Belief Networks (BBNs) are useful in modeling complex situations. Such graphical models help in giving better insight and understanding of the situation. Many algorithms for machine learning of BBN structures have been developed. In this paper six different algorithms have been reviewed by constructing BBN structures for two different datasets using various algorithms. Some inferences have been drawn from the results obtained from the study which may help in decision making.
Keywords :
belief networks; inference mechanisms; learning (artificial intelligence); BBN structures; Bayesian belief network structure learning algorithms; decision making; graphical models; machine learning; Bayesian methods; Diseases; Inference algorithms; Learning systems; Machine learning algorithms; Size measurement; Bayesian Belief Network; Hepatitis Domain Diseases; Structure Learning; Urinary System Diseases;
Conference_Titel :
Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0029-3
DOI :
10.1109/ICICS.2011.6173579
