Abstract
Building well-performing neural network models often require a large amount of real data. However, synthetic datasets are favoured as the collection of real data often brings up privacy and data security issues. This paper aims to build a shallow neural network model for the pre-trained synthetic feature dataset, VehicleX. Using genetic algorithm to reduce the dimensional complexity by randomly selecting a subset of features from before training. Furthermore, distinctiveness pruning techniques are used to reduce the network structure and attempt to find the optimal hidden neuron size. Both techniques improve the model performance in terms of test accuracy. The baseline model achieves 36.07% classification accuracy. Integrating genetic algorithm and the distinctiveness pruning achieve approximately 37.26% and 36.12% respectively while combining both methods achieve 37.31%.
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Zhang, L., Fu, YS. (2021). Genetic Algorithm and Distinctiveness Pruning in the Shallow Networks for VehicleX. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_8
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