Dealing with the task of imbalanced, multidimensional data classification using ensembles of exposers

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Paweł Ksieniewicz, Michał Woźniak ;
Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:164-175, 2017.

Abstract

Recently, the problem of imbalanced data is the focus of intense research of machine learning community. Following work tries to utilize an approach of transforming the data space into another where classification task may become easier. Paper contains a proposition of a tool, based on a photographic metaphor to build a classifier ensemble, combined with a random subspace approach. Developed solution is insensitive to a sample size and robust to dimension increase, which allows a regularization of feature space, reducing the impact of biased classes. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark and synthetic datasets.

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