A memetic algorithm based extreme learning machine for classification

Yongshan Zhang, Zhihua Cai, Jia Wu, Xinxin Wang, Xiaobo Liu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)

Abstract

Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme Learning Machine (M-ELM) for classification problems. M-ELM uses Memetic Algorithm which is a combination of population-based global optimization technique and individual-based local heuristic search method to find optimal network parameters for ELM. The optimized network parameters will enhance the classification accuracy and generalization performance of ELM. Experiments and comparisons on 22 benchmark data sets demonstrate that M-ELM is able to provide highly competitive results compared with other state-of-the-art varieties of ELM algorithms.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
Volume2015-September
ISBN (Electronic)9781479919604
ISBN (Print)9781479919598
DOIs
Publication statusPublished - 28 Sep 2015
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period12/07/1517/07/15

Keywords

  • Annealing
  • Classification algorithms
  • Cotton
  • Glass
  • Iris
  • Optimization
  • Random access memory

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