Learning the Markov blanket(MB)of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables.Online MB Learning can lear...Learning the Markov blanket(MB)of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables.Online MB Learning can learn MB for a given variable on the fly.However,in some application scenarios,such as image analysis and spam filtering,features may arrive by groups.Existing online MB learning algorithms evaluate features individually,ignoring group structure.Motivated by this,we formulate the group MB learning with streaming features problem,and propose an Online MB learning with Group Structure algorithm,OMBGS,to identify the MB of a class variable within any feature group and under current feature space on the fly.Extensive experiments on benchmark Bayesian network datasets demonstrate that the proposed algorithm outperforms the state-of-the-art standard and online MB learning algorithms.展开更多
基金supported by the National Natural Science Foundation of China[No.62272001,No.U1936220,No.61872002,No.61876206 and No.62006003]The National Key Research and Development Program of China[No.2019YFB1704101]the Natural Science Foundation of Anhui Province of China[No.2108085QF270].
文摘Learning the Markov blanket(MB)of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables.Online MB Learning can learn MB for a given variable on the fly.However,in some application scenarios,such as image analysis and spam filtering,features may arrive by groups.Existing online MB learning algorithms evaluate features individually,ignoring group structure.Motivated by this,we formulate the group MB learning with streaming features problem,and propose an Online MB learning with Group Structure algorithm,OMBGS,to identify the MB of a class variable within any feature group and under current feature space on the fly.Extensive experiments on benchmark Bayesian network datasets demonstrate that the proposed algorithm outperforms the state-of-the-art standard and online MB learning algorithms.
