Learning Bayesian Network from Small Scale Dataset and Application
Yi Liu, Hkust Shenzhen · 2011
An efficient algorithm FCLBN for learning Bayesian network from small scale dataset was proposed.FCLBN uses the method of bootstrap to re-sample from the small scale dataset,and estimates the high confidence features of the source small scale dataset from the Bayesian networks learned from the re-sampling small datasets.The high confidence features are taken to guide the search of the best Bayesian network on the source dataset.After being evaluated on the standard benchmark dataset,FCLBN is applied to predict yeast protein localization.The result of the experiments indicates that the FCLBN algorithm can learn relatively accurate network from small scale dataset.