Hellinger distance based drift detection for nonstationary environments

Gregory Ditzler, Robi Polikar

46 Scopus citations

Abstract

Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed. There are many real-world problems where the distribution of the data changes as a function of time. Changes in nonstationary data distributions can significantly reduce the generalization ability of the learning algorithm on new or field data, if the algorithm is not equipped to track such changes. When the stationary data distribution assumption does not hold, the learner must take appropriate actions to ensure that the new/relevant information is learned. On the other hand, data distributions do not necessarily change continuously, necessitating the ability to monitor the distribution and detect when a significant change in distribution has occurred. In this work, we propose and analyze a feature based drift detection method using the Hellinger distance to detect gradual or abrupt changes in the distribution.

Keywords

  • concept drift
  • drift detection
  • nonstationary environments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

Cite this

Ditzler, G., & Polikar, R. (2011). Hellinger distance based drift detection for nonstationary environments. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDUE 2011: 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (pp. 41-48). [5948491] (IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDUE 2011: 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments). https://doi.org/10.1109/CIDUE.2011.5948491