Incremental learning from unbalanced data

Michael Muhlbaier, Apostolos Topalis, Robi Polikar

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

13 Scopus citations

Abstract

An ensemble based algorithm, Learn++.MT2, is introduced as an enhanced alternative to our previously reported incremental learning algorithm, Learn++. Both algorithms are capable of incrementally learning novel information from new datasets that consecutively become available, without requiring access to the previously seen data. In this contribution, we describe Learn++.MT2 which specifically targets incrementally learning from distinctly unbalanced data, where the amount of data that become available varies significantly from one database to the next. The problem of unbalanced data within the context of incremental learning is discussed first, followed by a description of the proposed solution. Initial, yet promising results indicate considerable improvement on the generalization performance and the stability of the algorithm.

Original languageEnglish (US)
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1057-1062
Number of pages6
DOIs
StatePublished - Dec 1 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period7/25/047/29/04

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Muhlbaier, M., Topalis, A., & Polikar, R. (2004). Incremental learning from unbalanced data. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1057-1062). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2). https://doi.org/10.1109/IJCNN.2004.1380080