Multiple classifiers based incremental learning algorithm for learning in nonstationary environments

Michael D. Muhlbaier, Robi Polikar

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

21 Scopus citations

Abstract

We describe an incremental learning algorithm designed to learn in challenging non-stationary environments, where the underlying data distribution that governs the classification problem changes at an unknown rate. The algorithm is based on a multiple classifier system that generates a new classifier every time a new dataset becomes available from the changing environment. We consider the particularly challenging form of this problem, where we assume that the previously generated data points are no longer available, even if some of those points may still be relevant in the new environment. The algorithm employs a strategic weighting mechanism to determine the error of each classifier on the current data distribution, and then combines the classifiers using a dynamically weighted majority voting. We describe the implementation details of algorithm, and track its performance as a function of the environment's rate of change. We show that the algorithm is able to track the changing environment, even when the environment changes drastically over a short period of time.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages3618-3623
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: Aug 19 2007Aug 22 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume6

Other

Other6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
CountryChina
CityHong Kong
Period8/19/078/22/07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Theoretical Computer Science

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