Learning a Bayesian Network Classifier by Jointly Maximizing Accuracy and Information. Conference Paper uri icon

abstract

  • Although recent studies have shown that a Bayesian network classifier (BNC) that maximizes the classification accuracy (ie, minimizes the 0/1 loss function) is a powerful tool in knowledge representation and classification, this classifier focuses on the majority class, is usually uninformative about the distribution of misclassifications, and is insensitive to error severity (making no distinction between misclassification types). We propose to learn a BNC using an information measure (IM) that jointly maximizes classification and information, and evaluate this measure using various databases. We show that an IM-based BNC is superior to BNCs learned using other measures, especially for ordinal classification and imbalanced problems, and does not fall behind state-of-the-art algorithms with respect to accuracy and amount of information provided.

publication date

  • January 1, 2016

presented at event