An ensemble based incremental learning framework for concept drift and class imbalance

Gregory Ditzler, Robi Polikar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations

Abstract

We have recently introduced an incremental learning algorithm, Learn ++.NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn++.NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature available for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment.

Original languageEnglish (US)
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period7/18/107/23/10

Keywords

  • concept drift
  • ensemble of classifiers
  • imbalanced data
  • incremental learning in nonstationary environments

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Ditzler, G., & Polikar, R. (2010). An ensemble based incremental learning framework for concept drift and class imbalance. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 [5596764] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2010.5596764