Heuristic Updatable Weighted Random Subspaces for non-stationary environments

T. Ryan Hoens, Nitesh V. Chawla, Robi Polikar

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

25 Scopus citations

Abstract

Learning in non-stationary environments is an increasingly important problem in a wide variety of real-world applications. In non-stationary environments data arrives incrementally, however the underlying generating function may change over time. While there is a variety of research into such environments, the research mainly consists of detecting concept drift (and then relearning the model), or developing classifiers which adapt to drift incrementally. We introduce Heuristic Updatable Weighted Random Subspaces (HUWRS), a new technique based on the Random Subspace Method that detects drift in individual features via the use of Hellinger distance, a distributional divergence metric. Through the use of subspaces, HUWRS allows for a more finegrained approach to dealing with concept drift which is robust to feature drift even without class labels. We then compare our approach to two state of the art algorithms, concluding that for a wide range of datasets and window sizes HUWRS outperforms the other methods.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages241-250
Number of pages10
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/14/11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Hoens, T. R., Chawla, N. V., & Polikar, R. (2011). Heuristic Updatable Weighted Random Subspaces for non-stationary environments. In Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011 (pp. 241-250). [6137228] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2011.75