A Comprehensively Improved Hybrid Algorithm for Learning Bayesian Networks: Multiple Compound Memory Erasing

TL;DR

This paper introduces MCME, a hybrid algorithm for learning Bayesian networks that improves accuracy and efficiency by addressing limitations of existing methods through innovative scoring functions and memory erasing techniques.

Contribution

The paper proposes a novel hybrid algorithm, MCME, combining advantages of constraint-based and score-based methods with innovative scoring and memory erasing for better Bayesian network learning.

Findings

MCME outperforms existing algorithms in accuracy and efficiency.

MCME effectively addresses high-dimensional CI test inaccuracies.

MCME demonstrates comparable or superior performance in experiments.

Abstract

Using a Bayesian network to analyze the causal relationship between nodes is a hot spot. The existing network learning algorithms are mainly constraint-based and score-based network generation methods. The constraint-based method is mainly the application of conditional independence (CI) tests, but the inaccuracy of CI tests in the case of high dimensionality and small samples has always been a problem for the constraint-based method. The score-based method uses the scoring function and search strategy to find the optimal candidate network structure, but the search space increases too much with the increase of the number of nodes, and the learning efficiency is very low. This paper presents a new hybrid algorithm, MCME (multiple compound memory erasing). This method retains the advantages of the first two methods, solves the shortcomings of the above CI tests, and makes innovations in the scoring function in the direction discrimination stage. A large number of experiments show that MCME has better or similar performance than some existing algorithms.