The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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...
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my-unisza-ir.33382022-01-09T06:34:06Z http://eprints.unisza.edu.my/3338/ The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data Mohamad, Prof. Madya Ts. Dr. Mumtazimah Abdul Rahman, Prof. Dr. Mohd Nordin Makhtar, Prof. Ts. Dr. Mokhairi QA75 Electronic computers. Computer science QA76 Computer software 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. Springer International Publishing 2016 Book Section NonPeerReviewed text en http://eprints.unisza.edu.my/3338/1/FH05-FIK-17-07718.pdf text en http://eprints.unisza.edu.my/3338/2/FH05-FIK-17-07727.pdf 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 |
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QA75 Electronic computers. Computer science QA76 Computer software Mohamad, Prof. Madya Ts. Dr. Mumtazimah Abdul Rahman, Prof. Dr. Mohd Nordin Makhtar, Prof. Ts. Dr. Mokhairi The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
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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. |
format |
Book Section |
author |
Mohamad, Prof. Madya Ts. Dr. Mumtazimah Abdul Rahman, Prof. Dr. Mohd Nordin Makhtar, Prof. Ts. Dr. Mokhairi |
author_facet |
Mohamad, Prof. Madya Ts. Dr. Mumtazimah Abdul Rahman, Prof. Dr. Mohd Nordin Makhtar, Prof. Ts. Dr. Mokhairi |
author_sort |
Mohamad, Prof. Madya Ts. Dr. Mumtazimah |
title |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
title_short |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
title_full |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
title_fullStr |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
title_full_unstemmed |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data |
title_sort |
reconstructed heterogeneity to enhance ensemble neural network for large data |
publisher |
Springer International Publishing |
publishDate |
2016 |
url |
http://eprints.unisza.edu.my/3338/1/FH05-FIK-17-07718.pdf http://eprints.unisza.edu.my/3338/2/FH05-FIK-17-07727.pdf http://eprints.unisza.edu.my/3338/ |
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1724079258726301696 |