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# Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data

*Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications*, PMLR 154:90-103, 2021.

#### Abstract

The number of rule-based classifiers specialized for imbalanced data is quite small so far. In particular, there is no such classifier dedicated for multi-class imbalance data. Thus, in this work we considered two ways of extending BRACID, which is the effective algorithm for binary data. In the first approach, BRACID was used in the OVO ensemble along with modifications of the prediction aggregation strategy. The second approach modifies an induction of rules for multiple classes simultaneously, additionally combined with their post-pruning. Experiments showed that both approaches outperformed the baselines. Moreover, the second approach turned out to be better than OVO with respect to predictive results and producing a smaller number of rules.