An ensemble approach for incremental learning in nonstationary environments

Michael D. Muhlbaier, Robi Polikar

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

39 Scopus citations

Abstract

We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting - as used by most ensemble based nonstationary learning algorithms - a strategic weighting mechanism is employed that tracks the classifiers' performances over drifting environments to determine appropriate voting weights. Specifically, the proposed approach generates a single classifier for each dataset that becomes available, and then combines them through a dynamically modified weighted majority voting, where the voting weights themselves are computed as weighted averages of classifiers' individual performances over all environments. We describe the implementation details of this approach, as well as its initial results on simulated non-stationary environments.

Original languageEnglish (US)
Title of host publicationMultiple Classifier Systems - 7th International Workshop, MCS 2007, Proceedings
PublisherSpringer Verlag
Pages490-500
Number of pages11
ISBN (Print)9783540724810
DOIs
StatePublished - 2007
Event7th International Workshop on Multiple Classifier Systems, MCS 2007 - Prague, Czech Republic
Duration: May 23 2007May 25 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4472 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Multiple Classifier Systems, MCS 2007
Country/TerritoryCzech Republic
CityPrague
Period5/23/075/25/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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