Adaptive ensemble based learning in non-stationary environments with variable concept drift

Teo Susnjak*, Andre L.C. Barczak, Ken A. Hawick

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The aim of this paper is to present an alternative ensemble-based drift learning method that is applicable to cascaded ensemble classifiers. It is a hybrid of detect-and-retrain and constant-update approaches, thus being equally responsive to both gradual and abrupt concept drifts. It is designed to address the issues of concept forgetting, experienced when altering weights of individual ensembles, as well as real-time adaptability limitations of classifiers that are not always possible with ensemble structure-modifying approaches. The algorithm achieves an effective trade-off between accuracy and speed of adaptations in time-evolving environments with unknown rates of change and is capable of handling large volume data-streams in real-time.

Original languageEnglish
Title of host publicationNeural Information Processing. Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
EditorsKok Wai Wong, B. Sumudu U. Mendis, A. Bouzerdoum
PublisherSpringer
Pages438-445
Number of pages8
VolumePART 1
ISBN (Print)3642175368, 9783642175367
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 22 Nov 201025 Nov 2010
Conference number: 17th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Neural Information Processing, ICONIP 2010
Abbreviated titleICONIP 2010
Country/TerritoryAustralia
CitySydney, NSW
Period22/11/1025/11/10

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