Abstract
AbstractBayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC's primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependenc...
Highlights
Bayesian network (BN), which can be regarded as an annotated directed graph that encodes the probabilistic relationships among variables of interest 7, is a popular data mining technique used to predict the class of a test instance in classification
The selected algorithms are evaluated in terms of classification accuracy measured by ACC and ranking performance measured by area under the ROC curve ranking (AUC)
We empirically investigated three Bayesian network classifiers: Tree Augmented Naive Bayes (TAN), Averaged One-dependence Estimators (AODE), and Hidden Naive Bayes (HNB), in terms of classification accuracy (ACC) and the area under the ROC curve ranking (AUC)
Summary
Bayesian network (BN), which can be regarded as an annotated directed graph that encodes the probabilistic relationships among variables of interest 7, is a popular data mining technique used to predict the class of a test instance in classification. Each node corresponds to a variable, and the conditional probability table (CPT) associated with it contains the probability of each state of the variable given every possible combination of states of its parents. Each node is conditionally independent of its non-descendants given its parents. The BN structure can be exploited by the explicit representation of probabilistic relations in BN for a given problem domain. In this way, it makes incorporating domain knowledge in the BN model design easier.
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