Comparison of ensemble techniques for incremental learning of new concept classes under hostile non-stationary environments

Hussein Syed Mohammed, James Leander, Matthew Marbach, Robi Polikar

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

3 Scopus citations

Abstract

We have recently introduced Learn++, an incremental learning algorithm, inspired by the multiple classifiers structure of AdaBoost. Both algorithms generate an ensemble of classifiers trained on bootstrapped replicates of the training data, and the classifiers are then combined through a voting process. Learn+ however, generates additional ensembles as new data become available, and uses a different distribution update rule to resample the data. While AdaBoost was originally designed to improve the performance of a weak classifier, whether it can still achieve incremental learning through its ensemble structure is still an open question. In this paper, we compare the incremental learning ability of AdaBoost.M1 and Learn under very hostile nonstationary learning environments, which may introduce new concept classes. We also compare the algorithms under several combination rules to determine which of the three key components - ensemble structure, resampling procedure, or the combination rule - has the primary impact on incremental learning in nonstationary environments.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4838-4844
Number of pages7
ISBN (Print)1424401003, 9781424401000
DOIs
StatePublished - Jan 1 2006
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
Duration: Oct 8 2006Oct 11 2006

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume6
ISSN (Print)1062-922X

Other

Other2006 IEEE International Conference on Systems, Man and Cybernetics
CountryTaiwan, Province of China
CityTaipei
Period10/8/0610/11/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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