Bayes-N: An Algorithm for Learning Bayesian Networks from Data Using Local Measures of Information Gain Applied to Classification Problems

Assessment of Bayesian Estimators for Osteoporosis Analysis

“…Specifically, the AODE can be considered as an ensemble of SuperParent One-Dependence Estimators (SPODEs) because every attribute depends on the class and another shared attribute, which is designated as the superparent [12]. c) Bayes Net: This algorithm belongs to a family of constraint-based algorithms that use conditional independence tests (based on information measures) to decide if a pair of nodes is to be connected or disconnected [13]. The differences of Bayes-Net with respect to its relatives are the use of the Bonferroni correction to adjust the threshold used in every statistical test, the depth of the independence tests (i.e., how many variables are considered in the conditional set), and the inclusion of a parameter to control the significant percentage of information gain [13].…”

“…c) Bayes Net: This algorithm belongs to a family of constraint-based algorithms that use conditional independence tests (based on information measures) to decide if a pair of nodes is to be connected or disconnected [13]. The differences of Bayes-Net with respect to its relatives are the use of the Bonferroni correction to adjust the threshold used in every statistical test, the depth of the independence tests (i.e., how many variables are considered in the conditional set), and the inclusion of a parameter to control the significant percentage of information gain [13]. The experiments included the following steps: data cleaning and binarization; transformation of In data cleaning and binarization, records with inconsistent information were either corrected or deleted (if missing information); however, it was found that some of the variables associated with the diagnosis were already binarized (according to medical specialist).…”

“…This feature is advantageous when there are a large number of attributes, because global scoring functions cannot minimize error sites associated with the performance of the resulting network classification. Although this algorithm has good efficiency, it is not a good classifier [13].…”

“…The applicability of Bayesian classifiers such as the Bayes Net algorithm is not restricted only to health but encompasses several areas, as shown in research on the prediction of academic dropout at the University of Rio de Janeiro, suggesting sufficient accuracy in the use of the Bayes Net algorithm (89.0) [21]. Some authors attribute the evaluation measures of this algorithm to the fact that the classifier does not allow interactions between attributes, a characteristic that can provide greater wealth in modeling and understanding of the phenomenon under study [13].…”

“…In the case of Bayes Net, the Bayesian network is constructed by either adding or deleting arcs, considering only individual pairs in relation to compliance of the attributes; that is, the rest of the nodes are not considered in analysis [13]. This reason is why the information gain measures used in Bayes Net are referred to as "local"; in other words, the network construction does not depend on a function that evaluates the entire network each time a request for either insertion or removal of nodes or arcs is created.…”