# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
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\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \multicolumn{1}{c}{**Data Stream**} & \multicolumn{1}{c}{**Supervised Learning Algorithms**} & \multicolumn{1}{c}{**Active Learning Algorithms**} \\ \cline{2-11} \multicolumn{1}{c}{**Lab Stream**} & \multicolumn{1}{c}{**LB**} & \multicolumn{1}{c}{**MOOB**} & \multicolumn{1}{c  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \multicolumn{1}{c}{**Data Stream**} & \multicolumn{6}{c}{**Supervised Learning Algorithms**} & \multicolumn{6}{c}{**Active Learning Algorithms**} \\ \hline  & **LB** & **MOOB** & **ARFre** & **SRP** & **CSARF** & **ROSE** & **ROALE-DI** & **CALMID** & **MicroFool-E** & **AdaL-MID** \\ \hline Fixed\_Stream\_1 & 97.32 (2) & 95.75 (5) & 94.30 (6) & 91.32 (8) & 91.93 (7) & 97.29 (3) & 86.91 (10) & 96.96 (4) & 91.24 (9) & **97.74 (1)** \\ Fixed\_Stream\_2 & 96.66 (9) & 95.75 (5) & 94.24 (6) & 90.90 (9) & 921.1 (8) & 96.56 (4) & 89.91 (10) & 96.81 (2) & 92.44 (7) & **97.56 (1)** \\ Fixed\_Sud\_1 & 90.98 (5) & 81.60 (9) & 90.82 (8) & 86.01 (8) & 91.06 (4) & 93.66 (3) & 63.72 (10) & 93.85 (2) & 57.95 (7) & **94.97 (1)** \\ Fixed\_Sud\_2 & 89.44 (6) & 83.62 (8) & 92.12 (8) & 87.49 (7) & 92.76 (4) & 94.72 (2) & 71.81 (10) & 94.03 (7) & 76.84 (9) & **91.50 (1)** \\ Fixed\_G\_1 & 84.92 (6) & 78.85 (8) & 90.49 (5) & 74.20 (9) & 91.28 (4) & 92.62 (2) & 68.56 (10) & 93.77 (2) & 84.24 (7) & **94.48 (1)** \\ Fixed\_G\_2 & 88.45 (6) & 80.70 (8) & 90.36 (5) & 61.80 (9) & 91.79 (3) & 93.67 (2) & 99.48 (10) & 91.38 (4) & 85.30 (7) & **94.15 (1)** \\ Fixed\_MS\_IS84 (5) & 70.21 (8) & 86.45 (2) & 55.88 (7) & 90.26 (8) & 98.67 (3) & 86.33 (10) & 86.23 (4) & 64.58 (8) & **91.53 (1)** \\ Fixed\_Mix\_IGIG1 & 82.87 (6) & 72.09 (8) & 86.61 (4) & 52.87 (9) & 89.11 (3) & 89.19 (2) & 49.70 (10) & 85.83 (5) & 75.43 (7) & **89.43 (1)** \\ Fixed\_Mix\_SIG & 88.51 (4) & 76.80 (8) & 86.86 (6) & 73.14 (9) & 88.45 (5) & 89.49 (3) & 62.18 (10) & 89.78 (2) & 80.00 (7) & **91.35 (1)** \\ \hline Avg. Rank & 4.78 & 7.56 & 5.00 & 8.33 & 4.89 & 2.78 & 10.00 & 3.11 & 7.56 & **1.00** \\ \hline Var\_Stream\_1 & 96.07 (9) & 95.39 (5) & 93.58 (6) & 88.85 (9) & 89.20 (8) & 95.60 (4) & 87.10 (10) & 96.71 (2) & 91.70 (7) & **97.33 (1)** \\ \hline Var\_Stream\_2 & 95.78 (9) & 94.48 (9) & 91.78 (9) & 88.74 (9) & 90.54 (7) & 95.71 (4) & 82.49 (10) & 96.23 (2) & 98.93 (8) & **95.95 (1)** \\ \hline Var\_Sud\_1 & 92.15 (4) & 82.29 (8) & 94.20 (8) & 91.05 (8) & 83.11 (7) & 94.40 (2) & 76.77 (10) & 94.01 (3) & 83.08 (9) & **94.56 (1)** \\ \hline Var\_Sud\_1 & 90.31 (8) & 85.47 (9) & 81.69 (9) & 65.10 (10) & 86.69 (5) & 91.05 (2) & 75.52 (9) & 86.96 (4) & 82.44 (7) & **91.39 (1)** \\ \hline Var\_Grad\_1 & 85.96 (8) & 82.70 (9) & 89.72 (5) & 90.55 (4) & 89.61 (9) & 91.15 (2) & 78.40 (10) & 91.14 (3) & 83.57 (7) & **94.31 (1)** \\ \hline Var\_Grad\_2 & 78.95 (8) & 82.82 (7) & 87.96 (5) & 62.07 (10) & 88.77 (4) & 90.54 (3) & 75.57 (9) & 92.72 (2) & 87.19 (6) & **93.30 (1)** \\ \hline Var\_Mask\_IG1 & 80.77 (5) & 76.44 (8) & 80.08 (7) & 80.13 (6) & 86.63 (1) & 86.29 (2) & 59.17 (10) & 81.45 (4) & 72.17 (7) & 83.81 (1) \\ \hline Var\_Mix\_IGIG1 & 78.97 (7) & 75.85 (8) & 80.21 (6) & 82.85 (8) & 84.02 (8) & 84.68 (3) & 64.12 (10) & 82.63 (4) & 73.08 (8) & **85.33 (1)** \\ \hline Var\_Mix\_SIG & 88.30 (4) & 83.87 (8) & 84.27 (6) & 83.94 (7) & 89.50 (2) & 88.02 (5) & 71.21 (10) & 88.93 (3) & 81.37 (9) & **89.76 (1)** \\ \hline Avg.