## 3 Results
### Classification of Using FNC Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **ACC** & **LP** & **LR** & **SP** & **SR** \\ \hline LDA & 0.4602 (0.2284) & 0.7033 (0.2649) & 0.4208 (0.3646) & 0.2059 (0.0803) & 0.6229 (0.3296) \\ KNN & 0.2935 (0.0361) & **0.8348 (0.0878)** & 0.1482 (0.0448) & 0.2043 (0.0175) & **0.8749 (0.0816)** \\ LGR & 0.6889 (0.0278) & 0.8052 (0.0166) & 0.8068 (0.0331) & **0.2174 (0.0782)** & 0.2171 (0.0878) \\ SVM & 0.6315 (0.0630) & 0.7980 (0.0174) & 0.7209 (0.0920) & 0.2003 (0.0361) & 0.2729 (0.0856) \\ CNN & **0.7139 (0.0362)** & 0.7954 (0.0111) & **0.8648 (0.0458)** & 0.1794 (0.0832) & 0.1110 (0.0472) \\ \hline \hline \end{tabular} The CNN model used here contains two convolutional layers with a convolution kernel size of \(3\times 3\). In addition to LeakyRelu, a maximum pooling layer of size \(4\times 4\) is also used.  
\end{table} 
Table 4: Performance of the methods in FNC classification. Figure 6: Classification results using FNC matrix. Using the data of the subjects in the multi-object tracking (MOT) task to calculate the Pearson correlation coefficient to obtain the FNC matrix which is used as the input of the classifier. Since the FNC matrix itself is not a time course, so we chose CNN as the representative of the deep learning model for classification. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Weighted F1** \\ \hline LDA & 0.4723 \\ KNN & 0.2716 \\ LGR & **0.6588** \\ SVM & 0.6259 \\ CNN & **0.6558** \\ \hline \hline \end{tabular} In order to compare the performance of the classifiers when using FNC data for classification, the weighted F1 score is also calculated. CNN and LGR model achieved the highest weighted F1 score.  
\end{table} 
Table 5: Weighted F1 of the methods in FNC classification. biased towards the learning of the majority class, resulting in the performance of the minority class is poor. The SVM and LGR methods, although the accuracy is slightly lower, the performance is relatively balanced, which shows that in the classification of FNC data, the traditional classifier represented by SVM and LGR has more advantages. In addition, if we compare Table 1 with Table 4, we will find that for traditional models, LDA and KNN methods perform poorly on both data, while LGR performs better on FNC data, and the classification performance of SVM method on the two types of data are similar. For deep learning models, since independent component time series data has temporal features in addition to spatial features, the use of convolution-GRU network can comprehensively learn two features to achieve a better classification effect than using CNN alone. Comparing Table 2 with Table 5, it can be found that in the classification task of this study, deep learning models have better classification performance, while most traditional classifiers perform poorly. On the one hand, this shows that the deep learning model is more suitable for the classification task of this study than the traditional classification model. On the other hand, it also shows that the data used in this study have a high nonlinearity and are not suitable for classification with a linear classifier.