Dealing with multiple classes in online class imbalance learning

Shuo WANG, Leandro L. MINKU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

55 Citations (Scopus)

Abstract

Online class imbalance learning deals with data streams having very skewed class distributions in a timely fashion. Although a few methods have been proposed to handle such problems, most of them focus on two-class cases. Multi-class imbalance imposes additional challenges in learning. This paper studies the combined challenges posed by multiclass imbalance and online learning, and aims at a more effective and adaptive solution. First, we introduce two resampling-based ensemble methods, called MOOB and MUOB, which can process multi-class data directly and strictly online with an adaptive sampling rate. Then, we look into the impact of multi-minority and multi-majority cases on MOOB and MUOB in comparison to other methods under stationary and dynamic scenarios. Both multi-minority and multi-majority make a negative impact. MOOB shows the best and most stable Gmean in most stationary and dynamic cases.
Original languageEnglish
Title of host publicationIJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
PublisherAAAI press
Pages2118-2124
Number of pages7
ISBN (Print)9781577357704
DOIs
Publication statusPublished - Jul 2016
Externally publishedYes
Event25th International Joint Conference on Artificial Intelligence, IJCAI'16 - , United States
Duration: 9 Jul 201615 Jul 2016

Conference

Conference25th International Joint Conference on Artificial Intelligence, IJCAI'16
Country/TerritoryUnited States
Period9/07/1615/07/16

Bibliographical note

This work was supported by EPSRC (Grant No. EP/K001523/1). Xin Yao was also supported by a Royal Society Wolfson Research Merit Award.

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