Undersampled Majority Class Ensemble for highly imbalanced binary classification

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v94-ksieniewicz18a, title = {Undersampled Majority Class Ensemble for highly imbalanced binary classification}, author = {Ksieniewicz, Pawel}, booktitle = {Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {82--94}, year = {2018}, editor = {Torgo, Luís and Matwin, Stan and Japkowicz, Nathalie and Krawczyk, Bartosz and Moniz, Nuno and Branco, Paula}, volume = {94}, series = {Proceedings of Machine Learning Research}, month = {10 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v94/ksieniewicz18a/ksieniewicz18a.pdf}, url = {https://proceedings.mlr.press/v94/ksieniewicz18a.html}, 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.} }
Endnote
%0 Conference Paper %T Undersampled Majority Class Ensemble for highly imbalanced binary classification %A Pawel Ksieniewicz %B Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2018 %E Luís Torgo %E Stan Matwin %E Nathalie Japkowicz %E Bartosz Krawczyk %E Nuno Moniz %E Paula Branco %F pmlr-v94-ksieniewicz18a %I PMLR %P 82--94 %U https://proceedings.mlr.press/v94/ksieniewicz18a.html %V 94 %X 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.
APA
Ksieniewicz, P.. (2018). Undersampled Majority Class Ensemble for highly imbalanced binary classification. Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 94:82-94 Available from https://proceedings.mlr.press/v94/ksieniewicz18a.html.

Related Material