Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier

摘要

Although recent studies have shown that a Bayesian network classifier (BNC) that maximizes the classification accuracy (i.e., minimizes the 0/1 loss function) is a powerful tool in both knowledge representation and classification, this classifier: (1) focuses on the majority class and, therefore, misclassifies minority classes; (2) is usually uninformative about the distribution of misclassifications; and (3) is insensitive to error severity (making no distinction between misclassification types). In this study, we propose to learn the structure of a BNC using an information measure (IM) that jointly maximizes the classification accuracy and information, motivate this measure theoretically, and evaluate it compared with six common measures using various datasets. Using synthesized confusion matrices, twenty-three artificial datasets, seventeen UCI datasets, and different performance measures, we show that an IM-based BNC is superior to BNCs learned using the other measures—especially for ordinal classification (for which accounting for the error severity is important) and/or imbalanced problems (which are most real-life classification problems)—and that it does not fall behind state-of-the-art classifiers with respect to accuracy and amount of information provided. To further demonstrate its ability, we tested the IM-based BNC in predicting the severity of motorcycle accidents of young drivers and the disease state of ALS patients—two class-imbalance ordinal classification problems—and show that the IM-based BNC is accurate also for the minority classes (fatal accidents and severe patients) and not only for the majority class (mild accidents and mild patients) as are other classifiers, providing more informative and practical classification results. Based on the many experiments we report on here, we expect these advantages to exist for other problems in which both accuracy and information should be maximized, the data is imbalanced, and/or the problem is ordinal, whether the classifier is a BNC or not. Our code, datasets, and results are publicly available http://www.ee.bgu.ac.il/~boaz/software.