Scalable Real-Time Attributes Responsive Extreme Learning Machine

Authors

Hongbo Wang^*, , Yuejuan Yao, Xi Liu, Xuyan Tu

School of Computer and Communication Engineering, Beijing Key Lab of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Xueyuan Road 30, Haidian Zone, Beijing, 100083, China

^*Corresponding author. Email: foreverwhb@126.com

DOI

10.2991/ijcis.d.200731.001How to use a DOI?

Keywords

Extreme learning machine; Attributes scalable; Cropping strategy

Abstract

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, and ease of implementation. Its many applications, such as regression, binary and multiclass classification, acquired better results. However, when some attributes of the dataset have been lost, this fixed network structure will be less than satisfactory. This article suggests a Scalable Real-Time Attributes Responsive Extreme Learning Machine (Star-ELM), which can grow its appropriate structure with nodes autonomous coevolution based on the different dataset. Its hidden nodes can be merged to more effectively adjust structure and weight. In the experiments of classical datasets we compare with other relevant variants of ELM, Star-ELM makes better performance on classification learning with loss of dataset attributes in some situations.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hongbo Wang
AU  - Yuejuan Yao
AU  - Xi Liu
AU  - Xuyan Tu
PY  - 2020
DA  - 2020/08/10
TI  - Scalable Real-Time Attributes Responsive Extreme Learning Machine
JO  - International Journal of Computational Intelligence Systems
SP  - 1101
EP  - 1108
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200731.001
DO  - 10.2991/ijcis.d.200731.001
ID  - Wang2020
ER  -