论文信息 - Incremental learning of Bayesian networks with hidden variables

Incremental learning of Bayesian networks with hidden variables

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.

Fengzhan Tian | Hongwei Zhang | Yuchang Lu | Chunyi Shi

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