A kernel extreme learning machine algorithm based on improved particle swam optimization

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unst...

Full description

Saved in:
Bibliographic Details
Published in:Memetic computing 2017-06, Vol.9 (2), p.121-128
Main Authors: Lu, Huijuan, Du, Bangjun, Liu, Jinyong, Xia, Haixia, Yeap, Wai K.
Format: Article
Language:English
Subjects:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Classification
Complex Systems
Control
Engineering
Machine learning
Mathematical and Computational Engineering
Mechatronics
Optimization
Parameters
Regular Research Paper
Robotics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-016-0182-5