Sensitivity of multi-dimensional Bayesian classifiers

Abstract

One-dimensional Bayesian network classifiers (OBCs) are popular tools for classification [2]. An OBC is a Bayesian network [4] consisting of just a single class variable and several feature variables. Multi-dimensional Bayesian network classifiers (MBCs) were introduced to generalise OBCs to multiple class variables [1, 6]. Classification performance of OBCs is known to be rather good. Experimental results that support this observation were substantiated by a study of the sensitivity properties of naive OBCs [5]. In this paper we investigate the sensitivity of MBCs. We present sensitivity functions for the outcome probabilities of interest of an MBC and use these functions to study the sensitivity value. This value captures the sensitivity of an output probability to small changes in a parameter. We compare MBCs to OBCs in this respect and conclude that an MBC will on average be even more robust to parameter changes than an OBC.