New journal publication: Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics

The member of the JRL Eneko Osaba has recently participated in the development of the paper Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics, published in Applied Intelligence (Q2, 1.983).

The key topics of their research are: Ensemble classification, Imbalanced classification, Feature space partitioning, Hybrid metaheuristics .

Summary: One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.

DOI: https://doi.org/10.1016/j.scs.2018.10.001