EBRB cascade classifier for imbalanced data via rule weight updating

Abstract In recent years, data imbalance in the conventional classification problem has raised great interest in the industry. However, concerning the rule-based systems, this problem has been rarely investigated. We propose a Belief Rule-Based (BRB) reasoning model based on the evidential reasoning algorithm to fill in the gap by solving the problem caused by imbalanced data with regards to the rule-based systems. It utilizes the data-driven characteristics of Extended Belief Rule-Based (EBRB) and incorporates the data class rebalancing technique with the ensemble learning method of EBRB. In order to increase the number of rules that can promote the correct classification of minority classes in EBRB, we apply an adaptive data resampling strategy to expand the proportion of minority classes. On this basis, the data is divided into several EBRB base learners with different classification characteristics. During the iterative process, the rule weight of the base learner is dynamically adjusted according to the correct and incorrect classification, such that the classifier can pay more attention to the minority classes and reduce the bias of classification. Finally, the optimized EBRB base learner is selected to build an ensemble classifier with excellent classification performance. Experiments on 27 binary class and 12 multi-class imbalanced datasets show that our approach significantly improves F-value and MACC compared with other imbalanced classification algorithms. By building a cascaded EBRB model, the activation weight is taken as the basis of model evolution, the application limitations of the EBRB model in the ensemble learning are solved innovatively, and the classification performance of the EBRB model for imbalanced data is improved significantly.