A hierarchical classification model for class-imbalanced data

Traditional machine learning methods have lower classification performance when dealing with class imbalanced data. A hierarchical classification model for class imbalanced data was thus proposed. With an AdaBoost classifier as its basis classifier, the model builds mathematical models by the features and false positive rates of the classifier, and demonstrates that parameters of the hierarchical classification model could be calculated. First, the hierarchical classification tree was as the structure, and then the classification cost of the hierarchical classification tree mode was obtained as well as a quantitative and mathematical description of the features of each layer. Finally, the classification cost could be converted to a optimization problem, and the solving process of the optimization problem was given. Meanwhile, results of the hierarchical classification are presented. Experiments have been conducted on UCI dataset, and the results show that the proposed method has higher AUC and F-measure compared to many existing class-imbalanced learning methods.