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 ...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
AbstractMulti-dimensional classification aims at finding a function that assigns a vector of class v...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
One-dimensional Bayesian network classifiers (OBCs) are popular tools for classification [2]. An OBC...
Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological struc...
AbstractEmpirical evidence shows that naive Bayesian classifiers perform quite well compared to more...
Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophist...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
We introduce the family of multi-dimensional Bayesian network classifiers. These clas-sifiers includ...
In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifier...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to...
The naïve Bayes classifier is considered one of the most effective classification algorithms today, ...
AbstractBayesian networks (BNs) provide a powerful graphical model for encoding the probabilistic re...
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to...
Multidimensional Bayesian network classifiers have gained popularity over the last few years due to ...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
AbstractMulti-dimensional classification aims at finding a function that assigns a vector of class v...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
One-dimensional Bayesian network classifiers (OBCs) are popular tools for classification [2]. An OBC...
Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological struc...
AbstractEmpirical evidence shows that naive Bayesian classifiers perform quite well compared to more...
Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophist...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
We introduce the family of multi-dimensional Bayesian network classifiers. These clas-sifiers includ...
In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifier...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to...
The naïve Bayes classifier is considered one of the most effective classification algorithms today, ...
AbstractBayesian networks (BNs) provide a powerful graphical model for encoding the probabilistic re...
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to...
Multidimensional Bayesian network classifiers have gained popularity over the last few years due to ...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
AbstractMulti-dimensional classification aims at finding a function that assigns a vector of class v...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
Add to ORKG