Multi-objective Model Selection for Extreme Learning Machine

DOI

10.2991/icsnce-16.2016.131How to use a DOI?

Keywords

Eextreme learning machine; Generalization performance; Multi-objective optimization; Model selection

Abstract

Recently, Extreme Learning Machines(ELMs) have get successful application in the fields of classification and regression. However, the generalization performance of ELM will be decreased if there exits non-optimal input weights and hidden biases. To solve this problem, this paper introduced a new model selection method of ELM based on multi-objective optimization. This method views ELM model selection as a multi-objective global optimization problem, in which the generalization error and output weights are as optimization objectives. To accelerate the optimization speed, a fast Leave-one-out(LOO) error estimate of ELM is introduced to refer to the generalization error. Taking into account the contradiction between these two objectives, multi-objective comprehensive learning particle swarm optimization algorithm is utilized to find non-dominated solutions. Experiment on four UCI regression data sets are conducted.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Liyun Wang
AU  - Zhenshen Zhu
AU  - Bin Sun
PY  - 2016/07
DA  - 2016/07
TI  - Multi-objective Model Selection for Extreme Learning Machine
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 677
EP  - 682
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.131
DO  - 10.2991/icsnce-16.2016.131
ID  - Wang2016/07
ER  -