# Regularized Chained Deep Neural Network Classifier for Multiple Annotators
## 5 Results and Discussion
### Real-World Datasets Results
\begin{table}
\begin{tabular}{l|c c c c c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{l|l|c c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Input**} & \multicolumn{1}{c}{**Berg**} & \multicolumn{1}{c}{**Ionosphere**} & \multicolumn{1}{c}{**Fron**} & \multicolumn{1}{c}{**Tie-Dec-Pos**} & \multicolumn{1}{c}{**Ist**} & \multicolumn{1}{c}{**Wine**} & \multicolumn{1}{c}{**Segmentation**} & \multicolumn{1}{c}{**Velence**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Average**} \\  & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline CPC-COD & AUC(9) & 99.04 \(\pm\)0.94 & 72.21 \(\pm\)1.36 & 96.92 \(\pm\) 2.44 & 83.76 \(\pm\) 1.58 & 99.97 \(\pm\) 0.06 & 97.68 \(\pm\) 2.27 & 99.02 \(\pm\) 0.67 & 90.08 \(\pm\) 1.94 & 94.52 \(\pm\) 0.57 & 98.29 & - \\  & Acc(9) & 96.44 \(\pm\) 1.54 & 66.48 \(\pm\) 4.43 & 91.08 \(\pm\) 2.41 & 78.71 \(\pm\) 1.36 & 99.16 \(\pm\) 2.05 & 96.58 \(\pm\) 3.29 & 96.21 \(\pm\) 1.44 & 70.68 \(\pm\) 6.81 & 77.55 \(\pm\) 1.28 & 86.12 & - \\ \hline CPC-AMV & AUC(9) 98.24 \(\pm\) 2.45 & 67.60 \(\pm\) 6.91 & 82.91 \(\pm\) 6.03 & 70.18 \(\pm\) 6.03 & 98.51 \(\pm\) 1.52 & 96.53 \(\pm\) 1.55 & 97.29 \(\pm\) 1.38 & 77.09 \(\pm\) 1.81 & 77.40 \(\pm\) 4.94 & 80.13 & 7.44 \\  & Acc(9) & 84.63 \(\pm\) 2.26 & 84.27 \(\pm\) 5.84 & 78.58 \(\pm\) 6.03 & 66.52 \(\pm\) 5.14 & 64.86 \(\pm\) 3.64 & 88.51 \(\pm\) 5.00 & 86.22 \(\pm\) 5.76 & 88.29 \(\pm\) 4.99 & 68.01 \(\pm\) 7.23 & 75.67 \\ \hline MA-UC & AUC(9) 98.99 \(\pm\) 0.59 & 84.80 \(\pm\) 2.76 & 84.80 \(\pm\) 9.88 & 72.66 \(\pm\) 9.98 & 72.73 \(\pm\) 3.34 & 52.88 \(\pm\) 3.13 & 96.72 \(\pm\) 8.98 & 64.72 \(\pm\) 1.33 & **98.00** \(\pm\) 6.04 & 84.07 \(\pm\) 0.84 & 88.15 & 6.22 \\  & Acc(9) & 86.80 \(\pm\) 1.29 & 56.41 \(\pm\) 8.12 & 61.47 \(\pm\) 6.31 & 64.53 \(\pm\) 4.73 & 92.30 \(\pm\) 5.18 & 87.55 \(\pm\) 5.38 & 88.59 \(\pm\) 5.08 & 85.13 \(\pm\) 1.33 & 72.89 & 6.11 \\ \hline MA-DGE & AUC(9) & 93.91 \(\pm\) 0.42 & 67.47 \(\pm\) 7.72 & 77.78 \(\pm\) 7.20 & 81.66 \(\pm\) 2.54 & 55.00 \(\pm\) 1.05 & 96.73 \(\pm\) 1.53 & 95.36 \(\pm\) 3.32 & 93.43 \(\pm\) 8.04 & 86.14 \(\pm\) 1.20 & 85.91 & 4.46 \\  & Acc(9) & 78.03 \(\pm\) 2.22 & 55.84 \(\pm\) 4.73 & 78.35 \(\pm\) 1.35 & 78.09 \(\pm\) 1.09 & 46.22 \(\pm\) 7.26 & 86.84 \(\pm\) 1.32 & 96.83 \(\pm\) 2.09 & 68.41 \(\pm\) 1.28 & 75.24 & 5.55 \\ \hline MA-MAE & AUC(9) & 96.22 \(\pm\) 1.23 & 64.55 \(\pm\) 9.77 & 64.18 \(\pm\) 9.17 & 79.49 \(\pm\) 2.64 & 53.36 \(\pm\) 4.73 & 93.36 \(\pm\) 5.08 & 96.25 \(\pm\) 2.40 & 94.40 \(\pm\) 1.26 & 64.80 \(\pm\) 0.03 & 77.75 & 7.66 \\  & Acc(9) & 87.15 \(\pm\) 1.48 & 64.16 \(\pm\) 9.73 & 73.84 \(\pm\) 1.29 & 53.30 \(\pm\) 4.62 & 71.30 \(\pm\) 1.06 & 86.55 \(\pm\) 1.58 & 95.30 \(\pm\) 1.03 & 43.45 \(\pm\) 1.45 & 72.73 & 7.70 \\ \hline MA-CPC & AUC(9) & 85.37 \(\pm\) 1.30 & 83.75 \(\pm\) 1.20 & 83.45 \(\pm\) 4.37 & 73.37 \(\pm\) 4.33 & 64.52 \(\pm\) 5.91 & 87.11 \(\pm\) 1.14 & 99.00 \(\pm\) 4.91 & 93.34 \(\pm\) 5.90 & 88.71 \(\pm\) 5.55 & 85.55 \\  & Acc(9) & 92.85 \(\pm\) 1.27 & 85.63 \(\pm\) 6.98 & 69.87 \(\pm\) 4.41 & 62.30 \(\pm\) 5.30 & 62.33 \(\pm\) 2.29 & 90.58 \(\pm\) 3.59 & 89.09 \(\pm\) 2.92 & 94.66 \(\pm\) 0.03 & **78.18 \(\pm\) 1.39** & 77.70 & 4.44 \\ \hline KAIR & AUC(9) & 92.83 \(\pm\) 1.09 & 56.52 \(\pm\) 5.31 & 82.20 \(\pm\) 5.07 & 83.20 \(\pm\) 5.36 & 73.24 \(\pm\) 7.09 & 97.49 \(\pm\) 1.10 & 97.15 \(\pm\) 1.41 & 83.79 \(\pm\) 1.41 & 83.79 \(\pm\) 2.03 & 83.92 & 6.22 \\  & Acc(9) & 77.93 \(\pm\) 3.34 & 54.47 \(\pm\) 7.20 & 4.80 \(\pm\) 6.29 & 70.69 \(\pm\) 3.53 & 90.44 \(\pm\) 5.18 & 97.45 \(\pm\) 4.86 & 78.35 \(\pm\) 5.05 & 64.14 \(\pm\) 1.36 & 72.00 & 6.88 \\ \hline KAIR-LR & AUC(9) & 92.89 \(\pm\) 2.30 & **64.07 \(\pm\) 6.34** & 80.90 \(\pm\) 9.33 & 93.33 \(\pm\) 3.08 & 86.92 \(\pm\) 2.14 & 99.72 \(\pm\) 6.08 & 87.30 \(\pm\) 3.00 & 81.60 \(\pm\) 4.09 & 86.05 & 8.65 \\  & Acc(9) & 92.47 \(\pm\) 2.28 & **60.22 \(\pm\) 5.87** & 78.50 \(\pm\) 2.33 & 75.20 \(\pm\) 2.26 & 58.46 \(\pm\) 2.27 & 88.45 \(\pm\) 1.32 & 89.49 \(\pm\) 1.32 & 89.77 \(\pm\) 0.39 & 54.00 \(\pm\) 2.05 & 76.10 & 4.88 \\ \hline \hline \multirow{2}{*}{LKA-RAM-SVM} & AUC(9) 9.97 \In the case of music data, our RCDNN obtains the best classification performance.