Addressing imbalanced data with argument based rule learning

•We improve learning rules from imbalanced data by using the expert knowledge.•An expert explains the decision for some critical examples, giving arguments.•Three methods of identifying critical examples are proposed and compared.•Induced rules reflect the expert knowledge and better classify the minority examples.•Trade-off between the recognition of the minority and majority classes is maintained.

In this paper we focus on improving rule based classifiers learned from class imbalanced data by incorporating expert knowledge into the learning process. Applying expert knowledge should overcome limitations of standard methods for imbalanced data when minority classes contain many rare examples and outliers. It should also improve the minority class while maintaining better classification accuracy of the majority classes than the standard methods. Unlike existing proposals for integrating global expert knowledge into rule induction, the class imbalance requires considering local characteristics of class distributions. Therefore, we consider argument based learning, where a domain expert can annotate (explain) some of learning examples to describe reasons for assigning them to specific classes. Using local arguments should improve the interpretability of rules and their consistency with the domain knowledge, and should also result in a better recognition of the minority class. The main aim of our study is to show how argument based learning can be adapted to learn rules from imbalanced data. To achieve it, we introduce a new argument based rule induction algorithm ABMODLEM with a specialized classification strategy for imbalanced classes. Then, we propose new methods for identifying the examples which should be explained by an expert. They exploit the idea of active learning with the query by an ensemble. The proposed approach has been evaluated in an extensive computational experiment. Results show that argument based learning improves the minority class recognition, especially for difficult data distributions with rare examples and outliers. Moreover, ABMODLEM is compared against standard rule classifiers and their extensions with SMOTE preprocessing.