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Multi Sampling Random Subspace Ensemble for Imbalanced Data Stream Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 977)

Abstract

The classification of data streams is a frequently considered problem. The data coming in over time has a tendency to change its characteristics over time and usually we also encounter some difficulties in data distributions as inequality of the number of learning examples from considered classes. The combination of these two phenomena is an additional challenge. In this article, we propose a novel MSRS (Multi Sampling Random Subspace Ensemble) a chunk-based ensemble method for imbalanced non-stationary data stream classification. The proposed algorithm employs random subspace approach and balancing data using various sampling methods to ensure an appropriate diversity of the classifier ensemble. MSRS has been evaluated on the basis of the computer experiments carried out on the diverse pool of the non-stationary imbalanced data streams.

Keywords

Ensemble learning Imbalanced data Concept drift Data stream 

Notes

Acknowledgement

This work was supported by the Polish National Science Centre under the grant No. 2017/27/B/ST6/01325.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wrocław University of Science and TechnologyWrocławPoland

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