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 in...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/642/1/FH03-FIK-17-10509.pdf http://eprints.unisza.edu.my/642/ |
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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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