The reconstructed heterogeneity to enhance ensemble neural network for large data

Mumtazimah, Mohamad and Mokhairi, Makhtar and Mohd Nordin, Abdul Rahman (2017) The reconstructed heterogeneity to enhance ensemble neural network for large data. In: The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, 18-20 August 2016, Bandung; Indonesia.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Informatics & Computing
Depositing User: Muhammad Akmal Azhar
Date Deposited: 19 Nov 2020 07:22
Last Modified: 19 Nov 2020 07:22

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