Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning on High Dimensional Data

Abstract

Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. To address the above problem, we propose a novel Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well- known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing algorithms on limited datasets.