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 language | English |
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Title of host publication | Neural Information Processing. Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings |
Editors | Kok Wai Wong, B. Sumudu U. Mendis, A. Bouzerdoum |
Publisher | Springer |
Pages | 438-445 |
Number of pages | 8 |
Volume | PART 1 |
ISBN (Print) | 3642175368, 9783642175367 |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |
Event | 17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 22 Nov 2010 → 25 Nov 2010 Conference number: 17th |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 1 |
Volume | 6443 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th International Conference on Neural Information Processing, ICONIP 2010 |
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Abbreviated title | ICONIP 2010 |
Country/Territory | Australia |
City | Sydney, NSW |
Period | 22/11/10 → 25/11/10 |