Research Article
Autonomous Classification and Decision-Making Support of Citizen E-Petitions Based on Bi-LSTM-CNN
Table 6
Key parameters of the classification model to be optimized.
| Parameter | Explanation | Scope |
| Batch size | Number of samples per gradient update | | Learning rate | Hyperparameters in updating weights during gradient descent | | Embedding size | The dimensionality of the word vector | | Hidden size | Number of neurons in the hidden layer | | Kernel size | Length of convolution window in Bi-LSTM-CNN layer | | Kernel size | Length of convolution window in CNN layer | | Filter size | Number filters in Bi-LSTM-CNN layer | | Filter size | Number filters in CNN layer | | Dropout | A simple way to prevent neural networks from overfitting | | Activation function | A function for introducing nonlinear factors into a neural network | Relu or tanh |
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