Title :
Incremental learning of Bayesian networks with hidden variables
Author :
Tian, Fengzhan ; Zhang, Hongwei ; Lu, Yuchang ; Shi, Chunyi
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
An incremental method for learning Bayesian networks based on evolutionary computing, IEMA, is put forward. IEMA introduces the evolutionary algorithm and EM algorithm into the process of incremental learning; it can avoid getting into local maxima, and also incrementally learn Bayesian networks with high accuracy in the presence of missing values and hidden variables. In addition, we improved the incremental learning process by N. Friedman and M. Goldschmidt (1997). The experimental results verified the validity of IEMA. In terms of storage cost, IEMA is comparable with the incremental learning method of Friedman et al, while it is more accurate
Keywords :
belief networks; evolutionary computation; learning (artificial intelligence); Bayesian network learning; EM algorithm; evolutionary computing; hidden variables; incremental learning; incremental method; local maxima; missing values; storage cost; Bayesian methods; Computer networks; Computer science; Costs; Evolutionary computation; Intelligent networks; Intelligent systems; Laboratories; Learning systems; Statistics;
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.2001.989594
