# Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
## 3 Experiments  
The 44 datasets in the UCR archives [24], adopted by [PERSON] work [5], are employed by the most existing methods. In this scope, we consider the ones that fit our background question, which are picked from the following aspects: the number of training instances, number of their categories and their time step. Thus, validation experiments are carried out on 26 datasets. The training iteration of evaluator FCN in Algorithm 1 is set as 300 and that of the classifier FCN is 1600. The four hyper-parameters N, K, M and E in the method are up to specific dataset. Towards the special dataset ItPwDmd with only 24 dimensions, K = N is  
Figure 3: The commonly used structure of FCN in TSC field. taken, that means no elimination in time step. Settings of other hyper-parameters for the experiment are given in Table 1.  
Comparisons will be conducted between RSE-FCN and related benchmarks, including: (1) classical single modal deep learning TSC methods [5] FCN, ResNet and MLP, (2) the multimodal neural network MCNN [25] and MFCN [10], (3) non-deep-learning and deep-learning ensemble model: SE (Shaplet Ensemble), COTE [11] and NNE. In terms of the authors of the UCR archive also noted that \"beating the performance of DTW on some data sets should be considered a necessary\", we select the classical DTW [26] and an elastic -distance based ensemble PROP [12]. In addition, we conduct another sets of comparison among RSE-FCN, ENSR [14] and XG-BF [13] on several common datasets as well, since they are all the ensemble classifier built by learning different local discriminative information. Four evaluation metrics are adopted in above comparisons, they are the average accuracy, the times of wins, the win times excluding ties and the average ranking of each algorithm. Further, the average ranking with a critical distance (CD) given by hypothesis testing [27] could measure the performance difference. The experiments results are shown in Tables 2 and 3. Furthermore, Figure 4 is the CD diagram. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Setting** \\ \hline Batch size & 128 \\ Optimizer & Adam \\ Learning rate & \(10^{-3}\) to \(10^{-4}\) \\ Loss fuction & Cross-entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters of RSE-FCN validation experiment. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & **FCN** & **ResNet** & **MCNN** & **MLP** & **COTE** & **DTW** & **PROP** & **SE** & **NNE** & **MFCN** & **RSE-FCN** \\ \hline Adiac & 0.857 & 0.826 & 0.769 & 0.752 & 0.767 & 0.604 & 0.647 & 0.627 & 0.852 & 0.829 & **0.867** \\ Beef  
Observation shows that RSE-FCN is in the leading position among all models within comparison. Its performance is slightly better than NNE, the 60 deep learning classifier ensembles, and much better than FCN, COTE and all the other methods. Figure 4 shows most ensemble learning methods and deep learning methods are in the first level and they have no significant difference. However, RES-FCN ranks high in this group of methods, and exceeds the competitors in other specific metrics, and it is on a par with NNE. Through further comparative analysis, it can be concluded that:  
* According to the three metrics in Table 1, the proposed lightweight RSE-FCN consisted within 10 FCN is at the same level as NNE which constructed by 60 neural networks, even if excluding the judgment accident of NNE on dataset DiaSizeRed (Then, the average accuracy of NNE on 25 datasets will be approximately 0.892). Furthermore, if take that dataset into consideration, RES-FCN has an obvious advantage over NNE in accuracy and robustness. This is because the superior individual FCN employed in our method establishes more accurate results than that in NNE, and it has stable performance. The ensemble built by them makes further breakthrough from this basis. RSE-FCN outperforms three single modal neural networks and multimodal neural networks MCNN and MFCN.