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Relationship between Diversity and Perfomance of Multiple Classifiers for Decision Support
[article]
2008
arXiv
pre-print
The paper presents the investigation and implementation of the relationship between diversity and the performance of multiple classifiers on classification accuracy. The study is critical as to build classifiers that are strong and can generalize better. The parameters of the neural network within the committee were varied to induce diversity; hence structural diversity is the focus for this study. The hidden nodes and the activation function are the parameters that were varied. The diversity
arXiv:0810.3865v1
fatcat:aatpiom3s5d63lu4otoycsrysi