| _version_ |
1860797838477230080
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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 |
2024-08-27 11:03:44
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| format |
Restricted Document
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| id |
14332
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| institution |
UniSZA
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| originalfilename |
3338-01-FH05-FIK-17-07718.pdf
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Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/95.0.4638.69 Safari/537.36
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oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14332
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| spelling |
14332 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14332 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 2 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML like Gecko) Chrome/95.0.4638.69 Safari/537.36 2024-08-27 11:03:44 3338-01-FH05-FIK-17-07718.pdf UniSZA Private Access 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 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 AG Springer International Publishing AG 447-455 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 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.
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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
|