RFSEN-ELM: selective ensemble of extreme learning machines using rotation forest for image classification

Title:

RFSEN-ELM: selective ensemble of extreme learning machines using rotation forest for image classification

Creator:

Zhou, Z., Chen, J., Song, Y., Zhu, Z., and Liu, X.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4c6881eb-8d1a-46b8-95ec-178841f9a543
uuid:4c6881eb-8d1a-46b8-95ec-178841f9a543
doi:10.14311/NNW.2017.27.026

Subject:

neural network, extreme learning machine, rotation forest, genetic algorithm, selective ensemble model, and image classification

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

learning machine (ELM), as a new learning mechanism for single hidden layer feedforward neural networks (SLFNs), has shown its advantages, such as fast computation speed and good generalization performance. However, the weak robustness of ELM is an unavoidable defect for image classification. To address the problem, we propose a novel ensemble method which combines rotation forest and selective ensemble model in this paper. Firstly, ELM and rotation forest are integrated to construct an ensemble classifier (RF-ELM), which combines the advantages of both rotation forest and ELM. The purpose of rotation forest here is to enhance the diversity of each base classifier which can improve the performance generalization. Then several ELMs are removed from the ensemble pool by using genetic algorithm (GA) based selective ensemble model to further enhance the robustness. Finally, the remaining ELMs are grouped as a selected ensemble classifier (RFSEN-ELM) for image classification. The performance is analyzed and compared with several existing methods on benchmark datasets and the experimental results demonstrate that the proposed algorithm substantially improves the accuracy and robustness of classification at an acceptable level of training cost.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

499-517

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:5

Harvested from:

CDK

Metadata only:

false