Dynamically weighted majority voting for incremental learning and comparison of three boosting based approaches

Aliasgar Gangardiwala, Robi Polikar

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

18 Scopus citations

Abstract

We have previously introduced Learn++, an ensemble based incremental learning algorithm for acquiring new knowledge from data that later become available, even when such data introduce new classes. In this paper, we describe a modification to this algorithm, where the voting weights of the classifiers are updated dynamically based on the location of the test input in the feature space. The new algorithm provides improved performance, stronger immunity to catastrophic forgetting and finer balance to the stability-plasticity dilemma than its predecessor, particularly when new classes are introduced. The modified algorithm and its performance, as compared to Adaboost.M1 and the original Learn++, on real and benchmark datasets are presented.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1131-1136
Number of pages6
DOIs
StatePublished - Dec 1 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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    Gangardiwala, A., & Polikar, R. (2005). Dynamically weighted majority voting for incremental learning and comparison of three boosting based approaches. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 1131-1136). [1556012] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2). https://doi.org/10.1109/IJCNN.2005.1556012