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Effect of varying hidden neurons and data size on clusters, layers, diversity and accuracy in neural ensemble classifier

conference contribution
posted on 06.12.2017, 00:00 by CY Chiu, Brijesh Verma
This paper presents an approach for finding the effect of varying hidden neurons and data size on various parameters in neural ensemble classifier. The approach is based on incrementing hidden neurons in base classifiers and training them by decrementing the training data and testing using exactly same size data. The experimental analysis of hidden neurons and data size on clusters, layers, diversity and accuracy in neural ensemble classifier is conducted and presented. The experiments have been conducted using 10 benchmark datasets from UCI machine learning repository. A detailed analysis and results showing the effect of hidden neurons and data size on clusters, layers, diversity and accuracy are presented.

History

Start Page

455

End Page

459

Number of Pages

5

Start Date

01/01/2013

Finish Date

01/01/2013

Location

Sydney, Australia

Publisher

IEEE Computer Society

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Institute for Resource Industries and Sustainability (IRIS); School of Engineering and Technology (2013- );

Era Eligible

Yes

Name of Conference

IEEE International Conference on Computational Science and Engineering

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