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Neural Processing Letters

, Volume 38, Issue 3, pp 465–486 | Cite as

Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning

  • Bilal Mirza
  • Zhiping Lin
  • Kar-Ann Toh
Article

Abstract

Most of the existing sequential learning methods for class imbalance learn data in chunks. In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). WOS-ELM is a general online learning method that alleviates the class imbalance problem in both chunk-by-chunk and one-by-one learning. One of the new features of WOS-ELM is that an appropriate weight setting for CIL is selected in a computationally efficient manner. In one-by-one learning of WOS-ELM, a new sample can update the classification model without waiting for a chunk to be completed. Extensive empirical evaluations on 15 imbalanced datasets show that WOS-ELM obtains comparable or better classification performance than competing methods. The computational time of WOS-ELM is also found to be lower than that of the competing CIL methods.

Keywords

Class imbalance Online sequential learning Extreme learning machine (ELM) Weighted least squares Total error rate 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers whose insightful and helpful comments greatly improved this paper.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Electrical and Electronic EngineeringYonsei UniversitySeodaemun-guSouth Korea

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