GEP-based classifier for mining imbalanced data

The paper proposes an incremental Gene Expression Programming classifier for mining imbalanced datasets. Imbalanced datasets are commonly encountered in real-life applications. There exist numerous algorithms, techniques, and tools which are proposed as suitable for dealing with imbalanced class distribution. Yet, none of them seems to be able to outperform all others in all possible applications. We believe that our approach can extend the available range of learners that have proven good performance in mining imbalanced data and imbalanced streams. The idea is to adapt the GEP classifier to requirements of the imbalanced data environment with reuse of the minority class instances, and application of the incremental learning paradigm. The paper offers an overview of the related work and a detailed description of the proposed incremental learner. An extensive computational experiment, based on data from the KEEL dataset repository, proves that in numerous cases the approach is competitive to other state-of-the-art learners.