File(s) not publicly available

Relationship between data size, accuracy, diversity and clusters in neural network ensembles

journal contribution
posted on 06.12.2017, 00:00 by CY Chiu, Brijesh Verma
This paper presents an approach for analyzing relationships between data size, cluster, accuracy and diversity in neural network ensembles. The main objective of this research is to find out the influence of data size such as number of patterns, number of inputs and number of classes on various parameters such as clusters, accuracy and diversity of a neural network ensemble. The proposed approach is based on splitting data sets into different groups using the data size, clustering data and conducting training and testing of neural network ensembles. The test data is same for all groups and used to test all trained ensembles. The experiments have been conducted on 15 UCI machine learning benchmark datasets and results are presented in this paper.

History

Volume

12

Issue

4

Start Page

1

End Page

11

Number of Pages

11

eISSN

1757-5885

ISSN

1469-0268

Location

United Kingdom

Publisher

Imperial College Press

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Journal

International journal of computational intelligence and applications.

Exports