An incremental learning algorithm for non-stationary environments and class imbalance

Gregory Ditzler, Robi Polikar, Nitesh Chawla

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

30 Scopus citations

Abstract

Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary environments. However, this algorithm does not work well when there is class imbalance as it has not been designed to handle this problem. On the other hand, SMOTE - a popular algorithm that can handle class imbalance - is not designed to learn in nonstationary environments because it is a method of oversampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn++.NSE to create an algorithm that is robust to learning in a nonstationary environment and under class imbalance.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages2997-3000
Number of pages4
DOIs
StatePublished - Nov 18 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'An incremental learning algorithm for non-stationary environments and class imbalance'. Together they form a unique fingerprint.

Cite this