A novel under-sampling algorithm based on Iterative-Partitioning Filters for imbalanced classification

Xiaoshuang Chen, Qi Kang, Mengchu Zhou, Zhi Wei

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

5 Scopus citations

Abstract

Many real-world datasets suffer from the problem of class imbalance, i.e., they have a minority class being only a small portion of the whole dataset. Under-sampling techniques, e.g., EasyEnsemble (EE), present an efficient approach to imbalanced classification problems. However, imbalance is not the only factor that harms the performance of conventional classifiers. The presence of noises is another issue that really complicates the process of classifier learning. This paper presents a new noise-filtered under-sampling algorithm by incorporating an Iterative-Partitioning Filter (IPF) into EE, named EE-IPF for short. IPF can remove noises from both majority and minority classes. Comprehensive experiments are performed to test its performance via eleven commonly-used benchmark datasets. The results show its outstanding performance in terms of popular ly-used metrics for imbalanced classification.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PublisherIEEE Computer Society
Pages490-494
Number of pages5
ISBN (Electronic)9781509024094
DOIs
StatePublished - Nov 14 2016
Event2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States
Duration: Aug 21 2016Aug 24 2016

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2016-November
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
CountryUnited States
CityFort Worth
Period8/21/168/24/16

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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