Generally speaking, dependency analysis based Bayesian network learning algorithms are of higher efficiency. J. Cheng’s algorithm is a representative of this kinds of algorithms, while its efficiency could be improved further. This paper presents an efficient Bayesian network learning algorithm, which is an improvement to J. Cheng’s algorithm that uses Mutual Information (MI) and Conditional Mutual Information (CMI) as Conditional Independence (CI) tests. Through redefining the equations for calculating MI and CMI, our algorithm could decrease a large number of basic operations such as logarithms, divisions etc. and reduce the times of access to datasets to the minimum. Moreover, to efficiently calculate CMI, an efficient method for finding an approximate minimum cut-set is proposed in our algorithm. Experimental results show that under the same accuracy, our algorithm is much more efficient than J. Cheng’s algorithm.
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- An Improved Bayesian Network Learning Algorithm Based on Dependency Analysis
- Springer Berlin Heidelberg