Undersampled Majority Class Ensemble for highly imbalanced binary classification

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Pawel Ksieniewicz ;
Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 94:82-94, 2018.

Abstract

Following work tries to utilize an ensemble approach to solve a problem of highly imbalanced data classification. Paper contains a proposition of umce – a multiple classifier system, based on k-fold division of the majority class to create a pool of classifiers breaking one imbalanced problem into many balanced ones while ensuring the presence of all available samples in the training procedure. Algorithm, with five proposed fusers and a pruning method based on the statistical dependencies of the classifiers response on the testing set, was evaluated on the basis of the computer experiments carried out on the benchmark datasets and two different base classifiers.

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