Integer Bayesian network classifiers

Abstract

This paper introduces integer Bayesian network classifiers (BNCs), i.e. BNCs with discrete valued nodes where parameters are stored as integer numbers. These networks allow for efficient implementation in hardware while maintaining a (partial) probabilistic interpretation under scaling. An algorithm for the computation of margin maximizing integer parameters is presented and its efficiency is demonstrated. The resulting parameters have superior classification performance compared to parameters obtained by simple rounding of double-precision parameters, particularly for very low number of bits. © 2014 Springer-Verlag.