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- W4386735246 abstract "Multilayer perceptron (MLP) have been widely used in a variety of applications and fields. The hyper-parameters of such a machine learning (ML) models must be tuned to fit it into different problems. Choosing the best hyper-parameter configuration has a direct impact on the model performance. This work proposes a new optimization model, in order to find the optimal neural architecture solved by genetic algorithm method. We use a real architecture-representing chromosome that can express both the number of layers and the number of nodes in each layer. This novel proposed approach models the challenge of neural architecture optimization as non-linear constraint programming with mixed variables. The generalization potential of the MLP was further assessed, and the risk of over fitting was avoided, using a fold cross-validation technique. Results from the Iris dataset show an improvement in classification performance over earlier studies. The stability of the technique is also shown by the fact that the proposed method has the minimum of the mean accuracy rate’s standard deviation." @default.
- W4386735246 created "2023-09-15" @default.
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- W4386735246 date "2023-01-01" @default.
- W4386735246 modified "2023-09-27" @default.
- W4386735246 title "A Novel Model for Optimizing Multilayer Perceptron Neural Network Architecture Based on Genetic Algorithm Method" @default.
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- W4386735246 doi "https://doi.org/10.1007/978-3-031-43520-1_31" @default.
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