Distributed and weighted extreme learning machine for imbalanced big data learning

Zhiqiong Wang, Junchang Xin, Hongxu Yang, Shuo Tian, Ge Yu, Chenren Xu, Yudong Yao

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The Extreme Learning Machine (ELM) and its variants are effective in many machine learning applications such as Imbalanced Learning (IL) or Big Data (BD) learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM (DW-ELM) algorithm is proposed, which is based on the MapReduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated. Then, to further improve the computational efficiency, an Improved DW-ELM algorithm (IDW-ELM) is developed using only one MapReduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.

Original languageEnglish
Article number7889638
Pages (from-to)160-173
Number of pages14
JournalTsinghua Science and Technology
Volume22
Issue number2
DOIs
StatePublished - Apr 2017

Keywords

  • weighted Extreme Learning Machine (ELM); imbalanced big data; MapReduce framework; user-definedcounter

Fingerprint

Dive into the research topics of 'Distributed and weighted extreme learning machine for imbalanced big data learning'. Together they form a unique fingerprint.

Cite this