| _version_ |
1860797842403098624
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2024-08-28 11:15:38
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| format |
Restricted Document
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| id |
14349
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| institution |
UniSZA
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| originalfilename |
3382-01-FH05-FIK-17-09478.pdf
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| person |
Adobe Acrobat Pro DC 20.6.20042
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14349
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| spelling |
14349 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14349 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 10 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Adobe Acrobat Pro DC 20.6.20042 2024-08-28 11:15:38 3382-01-FH05-FIK-17-09478.pdf UniSZA Private Access The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data This paper presents an enhanced approach for ensemble multi classifier of Artificial Neural Networks (ANN). The motivation of this study is to improve 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 Cham Springer Cham 549-556 Recent Advances on Soft Computing and Data Mining
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| spellingShingle |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| summary |
This paper presents an enhanced approach for ensemble multi classifier of Artificial Neural Networks (ANN). The motivation of this study is to improve 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.
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| title |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| title_full |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| title_fullStr |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| title_full_unstemmed |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| title_short |
The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data
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| title_sort |
reconstructed heterogeneity to enhance ensemble neural network for large data
|