# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
Rank & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ (Fixed) & 4.44 & 8.78 & 3.44 & 6.22 & 6.33 & 2.89 & 9.78 & 3.11 & 8.44 & **1.56** \\ \hline Var\_Stream\_1 & 94.97 (9) & 92.66 (7) & 94.98 (9) & 93.14 (6) & 91.78 (8) & 94.55 (5) & 87.33 (10) & 95.61 (2) & 90.29 (9) & **96.00 (1)** \\ var\_Stream\_2 & 95.71 (2) & 91.35 (7) & 94.37 (5) & 93.03 (9) & 91.24 (8) & 94.84 (8) & 85.06 (10) & 95.60 (10) & 98.03 (9) & **96.20 (1)** \\ var\_Sud\_1 & 93.57 (8) & 85.21 (8) & 91.43 (8) & 94.22 (8) & 88.57 (9) & 95.88 (10) & 88.66 (10) & 93.93 (4) & 91.94 (2) & 94.73 (10) \\ var\_Sud\_2 & 92.16 (8) & 83.25 (9) & 91.43 (5) & 90.06 (8) & 83.47 (7) & 93.04 (2) & 82.78 (10) & 92.69 (3) & 83.40 (8) & **93.29 (1)** \\ var\_Grad\_1 & 90.99 (6) & 85.21 (9) & 92.88 (5) & 94.03 (2) & 90.76 (7) & 93.43 (3) & 84.21 (10) & 93.14 (4) & 81.48 (8) & **94.55 (1)** \\ var\_Grad\_2 & 90.31 (6) & 83.77 (9) & 92.26 (4) & 90.47 (5) & 88.49 (7) & 92.94 (3) & 83.42 (10) & 93.37 (2) & 88.01 (8) & **93.94 (1)** \\ var\_Mix\_IS\& 85.76 (7) & 74.55 (8) & 88.03 (8) & 85.99 (8) & 85.93 (6) & **89.34 (1)** & 73.00 (10) & 86.97 (7) & 73.66 (8) & 88.83 (2) \\ var\_Mix\_IGG1 & 84.77 (7) & 75.55 (8) & 87.49 (4) & 88.68 (2) & 88.07 (6) & 88.83 (3) & 74.04 (10) & 86.17 (5) & 79.42 (8) & **99.68 (1)** \\ var\_Mix\_SIG & 92.28 (8) & 85.39 (8) & 90.27 (6) & 90.79 (5) & 89.00 (7) & 91.50 (4) & 81.76 (10) & 91.99 (3) & 84.71 (9) & **93.12 (1)** \\ \hline Avg. Rank & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ (Var) & 4.78 & 8.33 & 4.67 & 4.22 & 7.00 & 2.89 & 10.00 & 3.44 & 8.44 & **1.22** \\ \hline Covtype & 81.76 (6) & 74.44 (9) & 86.37 (2) & **88.88 (1)** & 75.12 (8) & 83.38 (4) & 69.74 (10) & 81.80 (5) & 76.01 (7) & 85.34 (3) \\ Shutte & \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**} \\ \cline{2-11} \multicolumn{1}{c}{} & **LB** & **MOOB** & **ARFre** & **SRP** & **CSARF** & **ROSE** & **ROALE-DI** & **CALMID** & **Microbial-E** & **Ada-ML-MID** \\ \hline Fixed\_Stream\_1 & 97.22 (2) & 94.76 (5) & 93.01 (6) & 89.93 (8) & 88.84 (10) & 96.96 (5) & 89.73 (9) & 96.79 (4) & 90.91 (7) & **97.56 (1)** \\ Fixed\_Stream\_2 & 97.09 (9) & 95.18 (5) & 93.76 (6) & 90.10 (8) & 88.68 (10) & 96.77 (4) & 89.77 (9) & 97.16 (2) & 91.34 (7) & 97.35 (1) \\ Fixed\_Sud\_1 & 93.63 (4) & 81.65 (9) & 92.09 (5) & 90.96 (6) & 85.80 (8) & 94.21 (3) & 76.83 (10) & 95.14 (2) & 89.07 (7) & **95.88 (1)** \\ Fixed\_Sud\_2 & 92.59 (5) & 83.38 (9) & 94.14 (4) & 91.00 (6) & 89.60 (7) & 94.96 (5) & 86.07 (10) & 95.19 (2) & 89.02 (9) & **95.32 (1)** \\ Fixed\_C\_1 & 91.08 (5) & 79.74 (10) & 91.78 (4) & 87.97 (6) & 86.10 (7) & 93.83 (3) & 80.03 (9) & 95.51 (2) & 86.04 (8) & **95.85 (1)** \\ Fixed\_C\_2 & 92.74 (5) & 81.21 (9) & 93.21 (8) & 85.90 (8) & 87.97 (6) & 94.81 (2) & 76.34 (10) & 94.18 (3) & 87.60 (7) & **95.35 (1)** \\ Fixed\_MS\_SS1 & 85.40 (5) & 69.80 (9) & 90.00 (2) & 84.63 (7) & 84.76 (8) & 89.75 (3) & 68.39 (10) & 89.28 (4) & 71.59 (8) & **90.16 (1)** \\ Fixed\_Mix\_SG1 & 86.52 (5) & 71.52 (9) & 90.04 (2) & 82.16 (7) & 84.11 (6) & 89.33 (3) & 65.40 (10) & 88.57 (4) & 78.36 (8) & **90.27 (1)** \\ Fixed\_Mix\_SG1 & 90.18 (3) & 76.68 (9) & 88.19 (5) & 85.82 (6) & 83.58 (7) & 88.86 (4) & 72.95 (10) & 90.99 (2) & 80.54 (8) & **91.53 (1)** \\ \hline Avg.