Instance cloned extreme learning machine

Yongshan Zhang, Jia Wu*, Chuan Zhou, Zhihua Cai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems (i.e., data sets) with a smaller number of samples (i.e., instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data's distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms.

Original languageEnglish
Pages (from-to)52-65
Number of pages14
JournalPattern Recognition
Volume68
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Classification
  • Extreme Learning Machine
  • Instance cloning
  • Local learning

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