Integrating Data Selection and Extreme Learning Machine for Imbalanced Data

Umi Mahdiyah*, M. Isa Irawan, Elly Matul Imah

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

16 Citations (Scopus)


Extreme Learning Machine (ELM) is one of the artificial neural network method that introduced by Huang, this method has very fast learning capability. ELM is designed for balance data. Common problems in real-life is imbalanced data problem. So, for imbalanced data problem needs special treatment, because characteristics of the imbalanced data can decrease the accuracy of the data classification. The proposed method in this study is modified ELM to overcome the problems of imbalanced data by integrating the data selection process, which is called by Integrating the data selection and extreme learning machine (IDELM. Performances of learning method are evaluated using 13 imbalanced data from UCI Machine Learning Repository and Benchmark Data Sets for Highly Imbalanced Binary Classification (BDS). The validation includes comparison with some learning algorithms and the result showcases that average perform of our proposed learning method is compete and even outperform of some algorithm in some cases.

Original languageEnglish
Pages (from-to)221-229
Number of pages9
JournalProcedia Computer Science
Publication statusPublished - 2015
Event1st International Conference on Computer Science and Computational Intelligence, ICCSCI 2015 - Jakarta, Indonesia
Duration: 24 Aug 201526 Aug 2015


  • Data Selection
  • Extreme Learning Machine
  • Imbalanced Data


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