Weighted online sequential extreme learning machine for class imbalance learning

Bilal Mirza, Zhiping Lin, Kar Ann Toh

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)465-486
Number of pages22
JournalNeural Processing Letters
Issue number3
Publication statusPublished - 2013 Dec

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence


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