Towards Gaussian Bayesian Network Fusion
This work addresses the challenge of scalable learning for Bayesian networks in big data contexts, though it is incremental as an early insight into aggregation methods.
The paper tackles the problem of aggregating Bayesian network structures learned from separate datasets, as a step towards handling horizontally partitioned big data. It reports good results on synthetic data, surpassing individual learning outcomes.
Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order to be able to deal with what is nowadays referred to as Big Data. In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i.e. with respect to the instances, in order to be processed. Considerations that should be taken into account when dealing with this situation are discussed. Scalable learning of Bayesian networks is slowly emerging, and our method constitutes one of the first insights into Gaussian Bayesian network aggregation from different sources. Tested on synthetic data it obtains good results that surpass those from individual learning. Future research will be focused on expanding the method and testing more diverse data sets.