Training Normal Bayes Classifier on Distributed Data

Abstract

The paper describes an approach to parallelization of Normal Bayes classifier training algorithm for distributed data. In the process of distributed data analysis and the algorithm performance, the results fail to join properly. Due to this, the algorithm is to be performed in a distributed manner. For this purpose, we use representation of the algorithm as a sequential composition of functions. The algorithm is parallelized to work with data distributed horizontally and vertically. This allows placing parallel functions of the algorithm at data nodes. Experiments show that transfer of computations to sources allow to decrease training time and network traffic. We implement the algorithm variants as an extension of the industrial-strength Java-based library Xelopes.