The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data

Mohamad, Prof. Madya Ts. Dr. Mumtazimah and Abdul Rahman, Prof. Dr. Mohd Nordin and Makhtar, Prof. Ts. Dr. Mokhairi (2016) The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data. In: Recent Advances on Soft Computing and Data Mining. Springer International Publishing, pp. 447-455. ISBN 978-3-319-51279-2

[img] Text
FH05-FIK-17-07718.pdf
Restricted to Registered users only

Download (47kB)
[img] Text
FH05-FIK-17-07727.pdf
Restricted to Registered users only

Download (152kB)

Abstract

This paper present an enhanced approach for ensemble multi classifier of Artificial Neural Networks (ANN). The motivation of this study is to enhance the ANN capability and performance using reconstructed heterogeneous if the homogenous classifiers are deployed. The clusters set are partitioned into two sets of cluster; clusters of a same class and clusters of multi class which both of them were using different partition techniques. Each partitions represented by an independent classifier of highly correlated patterns from different classes. Each set of clusters are compared and the final decision is voted by using majority voting. The approach is tested on benchmark large dataset and small dataset. The results show that the proposed approach achieved almost near to 99% of accuracy which is better classification than the existing approach.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Informatics & Computing
Depositing User: Fatin Amirah Ramlan
Date Deposited: 09 Jan 2022 06:12
Last Modified: 09 Jan 2022 06:34
URI: http://eprints.unisza.edu.my/id/eprint/3338

Actions (login required)

View Item View Item