Research and Improvement of K2 Algorithm Based on Casual Effect
Bayesian network is an important model for reasoning in uncertain environments.Learning the structure of Bayesian networks correctly from data is a challenging task.To address the issue of the learning effectiveness of the K2 algorithm being influenced by the order of input nodes,a causal-effect-based Bayesian network structure learning algo-rithm is proposed based on Pearl's causal theory.Firstly,this algorithm learns the network node order using defined causal effect strengths.Secondly,the obtained network node order is input into the K2 algorithm to obtain an initial network struc-ture.Finally,the initial network structure is modified by removing edges based on the defined causal effect strengths.The experiments on the standard datasets Asia and Alarm for Bayesian networks demonstrate that this method exhibits good learning performance for both small-scale and large-scale networks.
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