# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
#### 4.3.4 Analysis of Algorithm Time Efficiency  
This section summarizes and analyzes the results of all comparison algorithms on 24 data streams, as shown in Table 7. The last row represents the average runtime across the 24 data streams. Supervised learning algorithms generally consume more time, since they require access to the labels of all instances in the data stream for training. Among them, MOOB, which uses only a single time-step-based oversampling strategy to handle imbalanced data streams, consumes relatively less time. However, because of the lack of a mechanism to handle concept drift, its performance in practical scenarios is not ideal. Following MOOB are ROSE, ARFre, and LB. LB employs a fixed-rate (\(\lambda=6\)) oversampling strategy, which increases the time consumption while failing to effectively address minority class instances in imbalanced data streams. ARFre and ROSE use dynamic resampling strategies that adjust the sampling rate based on changes in class proportions. However, unlike ROSE, which performs at different levels of oversampling for each instance, ARFre tends to reduce the probability of majority class instances appearing in the ARF trees, thus having a certain advantage in time efficiency. At the bottom are CSARF and SRP.  
The active learning algorithms ROALE-DI, CALMID, MicFoal-E, and AdaAL-MID can complete data stream learning with a limited number of instances under a label budget. In this study, the label budget was uniformly set to 0.2, meaning only 2 out of every 10 instances were selected for model training. The fastest in execution speed are ROALE-DI and MicFoal-E. However, ROALE-DI only considers the importance of minority class instances during the initialization of base classifiers, resulting in poor adaptability to changes in class imbalance in the data stream, thus leading to suboptimal performance. Similarly, MicFoal-E, lacking a mechanism to handle concept drift, performs poorly in continuously changing data streams. The proposed AdaAL-MID algorithm showed slightly better time efficiency than the recent comprehensive active learning algorithm CALMID and outperformed it on the vast majority of data streams. Moreover, compared to state-of-the-art supervised learning algorithms, such as SRP, ROSE, and CSARF, the AdaAL-MID achieved higher performance while consuming less time.  
Figure 6: Recall curves of the comparison algorithms on the real data streams.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Data Stream**}} & \multicolumn{6}{c}{**Supervised Learning Algorithms**} & \multicolumn{6}{c}{**Active Learning Algorithms**} \\ \cline{2-11}  & **LB** & **MOOB** & **ARFE** & **SRP** & **CSARF** & **ROSE** & **ROALE-DI** & **CALMID** & **Micro-E** & **Ada-ML-MD** \\ \hline Fixed\_Stream\_1 & 122.14 & 67.63 & 146.34 & 272.73 & 141.49 & 148.35 & 47.53 & 95.91 & 59.51 & 113.28 \\ Fixed\_Stream\_2 & 144.02 & 73.58 & 125.13 & 300.73 & 134.26 & 182.68 & 46.45 & 77.72 & 87.79 & 73.87 \\ Fixed\_Stad\_1 & 88.82 & 48.45 & 88.79 & 260.76 & 102.99 & 100.47 & 49.49 & 97.45 & 55.50 & 62.37 \\ Fixed\_Stad\_2 & 157.38 & 53.56 & 77.74 & 273.81 & 74.81 & 75.66 & 100.93 & 46.40 & 74.66 & 78.79 \\ Fixed\_Grad\_1 & 103.95 & 48.50 & 84.84 & 278.74 & 136.23 & 70.40 & 48.53 & 79.41 & 66.50 & 82.50 \\ Fixed\_Grad\_2 & 100.89 & 54.60 & 71.73 & 236.33 & 127.16 & 95.57 & 53.54 & 89.48 & 54.57 & 55.36 \\ Fixed\_Mask\_Stad\_1 & 101.97 & 51.54 & 78.78 & 252.37 & 82.86 & 101.57 & 47.53 & 81.44 & 106.86 & 103.50 \\ Fixed\_Mask\_Stad\_Stad\_Stad\_Stad\_Stad\_Stad