In order to obtain the Deep Learning network for the LR task, the architecture was first trained to classify the T- stage, with MCC = 0.863 on the training set (one-shot) and MCC = 0.279 on the test set, despite having used all the main techniques to avoid overfitting (e.g., use of Dropout Layers and Data Augmentation): here the small sample size of the dataset plays a critical role. 解释

这段话指的是为了得到用于LR任务的深度学习网络，首先训练了一个用于分类T-阶段的网络，该网络在训练集上的MCC（Matthews相关系数）为0.863（一次性训练），而在测试集上的MCC为0.279。尽管使用了所有主要的防止过拟合的技术（例如使用Dropout层和数据增强），但由于数据集样本量很小，这里的测试集效果不理想。

相关问题

Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

答：基于时间卷积注意网络（TCAN）和双向门驱动单元（BiGRU）网络，结合奇异谱分析（SSA）和改进量子粒子群优化算法（IQPSO）的联合预测模型，解决了传统网络安全态势预测方法依赖于历史态势值的准确性，以及各网络安全因子之间的相关性和重要性差异问题，在实验中，一方面将IQPSO与其他优化算法进行比较，结果表明IQPSO具有更好的优化性能；另一方面，通过仿真实验和建立的预测模型与传统预测方法进行比较，结果表明所建立的联合预测模型具有更高的预测准确性。

检查下列语句的语法和拼写问题。Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

我们可以看到，本文提出的综合预测模型基于时间卷积注意网络（TCAN）和双向门递归单元（BiGRU）网络，并通过奇异谱分析（SSA）和改进的量子粒子群优化算法（IQPSO）进行优化。实验表明，该模型具有更高的预测精度。