An ensemble method for incremental classification in stationary and non-stationary environments

Ricardo Ñanculef, Erick López, Héctor Allende, HÉCTOR GABRIEL ALLENDE CID

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a model based on ensemble of base classifiers, that are combined using weighted majority voting, for the task of incremental classification. Definition of such voting weights becomes even more critical in non-stationary environments where the patterns underlying the observations change over time. Given an instance to classify, we propose to define each voting weight as a function that will take into account the location of an instance to classify in the different class-specific feature spaces and also the prior probability of such classes given the knowledge represented by the classifier as well as its overall performance in learning its training examples. This approach can improve the generalization performance and ability to control the stability/plasticity tradeoff, in stationary and non-stationary environments. Experiments were carried out using several real classification problems already introduced to test incremental algorithms in stationary as well as non-stationary environments.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Pages541-548
Number of pages8
DOIs
StatePublished - 2011
Externally publishedYes
Event16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duration: 15 Nov 201118 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Iberoamerican Congress on Pattern Recognition, CIARP 2011
Country/TerritoryChile
CityPucon
Period15/11/1118/11/11

Keywords

  • Concept Drift
  • Dynamic Environments
  • Ensemble Methods
  • Incremental Learning

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