# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Streams** & **Instances** & **Attributes** & **Classes** & **Class Proportions** & **Drift Type (Number)** \\ \hline \multirow{8}{*}{Fixed class proportion} & Fixed\_Stream\_1 & 300 k & 20 & 5 & 5/4/3/2/1 & stable—no drift (0) \\  & Fixed\_Stream\_2 & 300k & 20 & 5 & 10/7/4/3/1 & stable—no drift (0) \\  & Fixed\_Sudd\_1 & 300 k & 20 & 5 & 10/7/4/3/1 & sudden (1) \\  & Fixed\_Sudd\_2 & 300 k & 20 & 5 & 19/5/5/4/1 & sudden (2) \\  & Fixed\_Grad\_1 & 300 k & 20 & 5 & 10/7/5/2/1 & gradual (1) \\  & Fixed\_Grad\_2 & 300 k & 20 & 5 & 28/10/6/5/1 & gradual (2) \\  & Fixed\_Mix\_ISSI & 300 k & 20 & 5 & 19/5/5/4/1 & sudden (2), increment (2) \\  & Fixed\_Mix\_IGGI & 300 k & 20 & 5 & 10/7/5/2/1 & gradual (2), increment (2) \\  & Fixed\_Mix\_SIG & 400 k & 20 & 5 & 28/10/6/5/1 & sudden (1), increment (1), \\ \hline \multirow{8}{*}{Variable class proportion} & Var\_Stream\_1 & 300 k & 20 & 5 & 5/4/3/2/1\(\rightarrow\)1/2/3/4/5 & sudden—virtual drift (1) \\  & Var\_Stream\_2 & 300 k & 20 & 5 & 10/7/4/3/1\(\rightarrow\)1/3/4/7/10 & sudden—virtual drift (1) \\  & Var\_Sudd\_1 & 300 k & 20 & 5 & 10/6/4/4/1\(\rightarrow\)1/2/5/7/10 & sudden (1) \\  & Var\_Sudd\_2 & 300 k & 20 & 5 & 19/5/5/4/1\(\rightarrow\)1/4/3/3/1\(\rightarrow\)1/4/5/5/19 & sudden (2) \\  & Var\_Grad\_1 & 300 k & 20 & 5 & 10/6/4/4/1\(\rightarrow\)1/2/5/7/10 & gradual (1) \\  & Var\_Grad\_2 & 300 k & 20 & 5 & 10/6/4/4/1\(\rightarrow\)28/10/6/5/1\(\rightarrow\)1/2/5/7/10 & gradual (2) \\  & Var\_Mix\_ISSI & 300 k & 20 & 5 & 19/5/5/4/1\(\rightarrow\)1/2/5/7/10\(\rightarrow\)28/10/6/5/1 & sudden (2), increment (2) \\  & Var\_Mix\_IGGI & 300 k & 20 & 5 & 10/7/5/2/1\(\rightarrow\)1/2/5/7/10\(\rightarrow\)28/10/6/5/1 & gradual (2), increment (2) \\  & Var\_Mix\_SIG & 400 k & 20 & 5 & 28/10/6/5/1\(\rightarrow\)1/4/5/5/19 & sudden (1), increment (1), \\ \hline \multirow{8}{*}{Real data stream} & Covtype & 581 k & 54 & 7 & - & - \\  & Shuttle & 58 k & 9 & 7 & - & - \\ \cline{1-1}  & hypothyroid & 1000 k & 29 & 4 & - & - \\ \cline{1-1}  & connect-4 & 6 k & 42 & 3 & - & - \\ \cline{1-1}  & diabetes & 254 k & 21 & 5 & - & - \\ \cline{1-1}  & CIC\_ISCX & 693 k & 68 & 6 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data stream characteristics. for minority class instances, while G-mean is the square root of the product of true positive rate (TPR) and true negative rate (TNR). Extended to the multiclass domain, recall represents the average of the recall rates for each class, while G-mean represents the nth root of the product of the recall rates for each class. Accuracy is one of the most commonly used evaluation metrics, representing the prediction performance for all instances. However, it may be less accurate in imbalanced data, so this paper comprehensively considered accuracy along with other metrics to fully evaluate the classifier performance. F1-score is the harmonic mean of precision and recall, attempting to balance these two metrics. Kappa evaluates classifier performance by calculating the consistency between predicted results and statistical distribution of the data, ranging from \([-1,1]\). A value of 0 indicates performance equivalent to a random classifier, while a value of 1 indicates the classification is completely correct.