bnclassify: Learning Bayesian Network Classifiers

Abstract

The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data. For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators. It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayesspecific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on mediumsized data sets. The bnclassify package provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network plotting via the Rgraphviz package. It is extensively tested, with over 200 automated tests that give a code coverage of 94%. Here we present the main functionalities, illustrate them with a number of data sets, and comment on related software.

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

“…The classifiers were built in the R [55] development environment with the use of the following packages: bnlearn [54], bnclassify [17], caret [56] oraz naivebayes [57].…”

“…Some of the methods studied qualified for the constraint-based approach, and some for the score-based structure learning approach. These methods are available in the bnlearn [54] and bnclassify [17] packages.…”

“…With this in mind, the research conducted analyses through a number of available score-based and constraint-based algorithms. As a result of these analyses, the optimal form of the DAG network was obtained for the learning method using Chow-Liu's tree Augmented Naive Bayes (TAN-CL) algorithm [17].…”

Circular Bayesian classifiers using wrapped Cauchy distributions

“…The experiments were run using R software [46]. To generate the artificial datasets to test the models, we used the ''Circular'' R package for the simulation of circular data, and to implement the structure of the wCTAN classifier, we have adapted the ''bnclassify'' R package [47]. Simulating data that follows wrapped Cauchy distributions is easy and computationally very fast.…”

Μοντελοποίηση Και Πρόβλεψη Διατμηματικών Και Διαχρονικών Βιοϊατρικών Δεδομένων Με Τη Χρήση Mπεϋζιανών Μεθόδων Και Γενικευμένων Γραμμικών Μοντέλων

“…The implementation of this work was conducted in R GUI 3.3.3 with the use of packages "bnlearn" and "bnclassify" [145,146]. In section 1.6 different BNC algorithms are introduced and in this work their performance in asthma exacerbation prediction after discontinuation of medication is compared.…”