Bayesian Networks with Structural Restrictions: Parallelization, Performance, and Efficient Cross-Validation

Bayesian Network algorithms are widely applied in the fields of bioinformatics, document classification, big data, and marketing informatics. In this paper, several Bayesian Network algorithms are evaluated, including Naive Bayes, Tree Augmented Naive Bayes, k-BAN, and k-BAN with Order Swapping. The algorithms are implemented using Scala and compared with the bnlearn library in R and Weka. Several datasets with varying numbers of attributes and instances are used to test the accuracy and efficiency of the implementations of the algorithms provided by the three packages. When handling huge datasets, issues involving accuracy, efficiency, and serial vs. parallel execution become more critical and should be addressed. We implemented several parallel algorithms as well as an efficient way to perform cross-validations, resulting in significant speedups.