# Deep Error-Correcting Output Codes
## 3 Deep Error-Correcting Output Codes (DeepECOCs)
### DeepECOCs
In the fourth one, to demonstrate the effectiveness of DeepECOCs on action recognition applications, we applied it to the CMU motion capture data set ([[http://mocap.cs.cmu.edu/](http://mocap.cs.cmu.edu/)]([http://mocap.cs.cmu.edu/](http://mocap.cs.cmu.edu/)) (accessed on 19 October 2023)). For all the data sets we mentioned above, to equate the scales of the data features and ease the training of the base classifiers, the features were normalized within \([0,1]\)[50]. In the fifth part, the CIFAR-10 data set ([[http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)]([http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 19 October 2023)) was used to demonstrate the effectiveness of DeepECOCs on image classification tasks compared with the related deep neural networks. Finally, we tested DeepECOCs on improving the effectiveness of CNNs. In the following, we present the experimental results in detail. ### Classification on 16 UCI Data Sets

In these experiments, we first compared the effect of using traditional SVMs against that using online SVMs to build the ECOC framework. The detail of the UCI data sets is shown in Table 1. We used 4 coding strategies, including one-versus-all, one-versus-one, DECOC and ECOCONE (initialized by one-versus-one). The linear loss-weighted (LLW) decoding strategy was applied for all the methods [9]. We chose SVMs with RBF kernel function as the traditional binary classifier (for short, LibSVM) [17]. The results based on the 10-fold cross validation are shown in Table 2. We can observe that incremental SVM performs better than LibSVM when applied to build the ECOC framework in most cases. Moreover, it is easy to handle the relative large-scale applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{2}{c}{**OneVsOne**} & \multicolumn{2}{c}{**OneVsAll**} & \multicolumn{2}{c}{**DECOC**} & \multicolumn{2}{c}{**ECOCONE**} \\ \cline{2-9}  & **LibSVM** & **I-SVM** & **LibSVM** & **I-SVM** & **LibSVM** & **I-SVM** & **LibSVM** & **I-SVM** \\ \hline Dermatology & 0.9671 & **0.9770** & 0.7928 & **0.9605** & **0.9671** & 0.9638 & 0.9671 & **0.9770** \\ Iris & 0.9333 & 0.9333 & 0.7030 & 0.7030 & 0.9333 & 0.9333 & 0.9333 & 0.9333 \\ Ecoli & 0.5944 & **0.6507** & 0.3940 & **0.4553** & 0.5281 & **0.5828** & 0.5944 & **0.6556** \\ Wine & 0.9892 & 0.9892 & 0.9731 & 0.9731 & **0.9892** & 0.9731 & 0.9839 & **0.9892** \\ Glass & **0.4838** & 0.4189 & 0.3216 & **0.3270** & 0.2973 & **0.3486** & 0.3027 & **0.5378** \\ Thyroid & 0.5897 & **0.6239** & 0.5897 & **0.6239** & **0.5897** & 0.4017 & 0.5897 & **0.6239** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy on the 16 UCI data sets obtained by comparing LibSVM and incremental SVM. The best results for each scenario are highlighted in bold face. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{2}{c}{**OneVsOne**} & \multicolumn{2}{c}{**OneVsAll**} & \multicolumn{2}{c}{**DECOC**} & \multicolumn{2}{c}{**ECOCONE**} \\ \cline{2-9}  & **LibSVM** & **I-SVM** & **LibSVM** & **I-SVM** & **LibSVM** & **I-SVM** & **LibSVM** \\ \hline Dermatology & 0.9671 & **0.9770** & 0.7928 & **0.9605** & **0.9671** & 0.9638 & 0.9671 & **0.9770** \\ Iris & 0.9333 & 0.9333 & 0.7030 & 0.7030 & 0.9333 & 0.9333 & 0.9333 & 0.9333 \\ Ecoli & 0.5944 & **0.6507** & 0.3940 & **0.4553** & 0.5281 & **0.5828** & 0.5944 & **0.6556** \\ Wine & 0.9892 & 0.9892 & 0.9731 & 0.9731 & **0.9892** & 0.9731 & 0.9839 & **0.9892** \\ Glass & **0.4838** & 0.4189 & 0.3216 & **0.3270** & 0.2973 & **0.3486** & 0.3027 & **0.5378** \\ Thyroid & 0.5897 & **0.6239** & 0.5897 & **0.6239** & **0.5897** & 0.4017 & 0.5897 & **0.6239** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the UCI data sets (I: Instances; A: attributes; C: classes). Then,we compared DeepECOCs with AE [26], DAE [27] and the single-layer ECOC framework [9].