# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Experimental Setup  
The experiment utilized synthetic data streams and real-world data streams. Table 1 presents detailed information about the data streams, including the number of instances, number of attributes, number of classes, specific class distribution, and types of concept drift. Among them, synthetic data streams were generated using MOA's RandomRBF and RandomTree stream generators to simulate different types of data streams. Subsequently, the synthetic data streams were used to test the classification performances of the AdaAL-MID under different conditions. In the synthetic data streams, two different types of data streams were set up. Lines 1-9 in Table 1 show the data streams with fixed class proportions. Lines 10-18 show data streams with dynamically changing class proportions. The changes in class proportions are combined with drift points, meaning that after a concept drift, the class distribution changes or reverses. Data streams Var_Stream_1 and Var_Stream_2 use virtual drift to trigger class proportion reversals. The real data streams included Covtype, Shuttle, hypothyroidism, connect-4, diabetes, and CIC_ISCX. These real-world data streams exhibit multiclass imbalance, but the number and types of concept drift are unknown, as shown in lines 19-24. The real data streams are described as follows:  
* Covtype ([[https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)]([https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)) (accessed on 5 March 2024)): This dataset contains forest resource data from Roosevelt County, Colorado, USA, and is designed to predict vegetation types in the forest. Specifically, the goal is to predict the vegetation type of each land plot based on various geographic and environmental features. * Shuttle ([[http://www.keel.es](http://www.keel.es)]([http://www.keel.es](http://www.keel.es)) (accessed on 5 March 2024)): this dataset originally comes from NASA's spacecraft landing control system simulation data and is used to distinguish among different flight states. * Hypothyroid ([[https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)]([https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)) (accessed on 5 March 2024)): widely used in medical diagnostics, particularly for the classification task of hypothyroidism. * Connect-4 ([[http://www.keel.es](http://www.keel.es)]([http://www.keel.es](http://www.keel.es)) (accessed on 5 March 2024)): This dataset is based on the classic board game \"Connect Four\" and is intended to predict the final outcome (win, loss, or draw) for the current player under a given game state. * Diabetes ([[https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset](https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset)]([https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset](https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset)) (accessed on 17 March 2024)): This dataset comes from the Behavioral Risk Factor Surveillance System (BRFSS) in the United States, which is a health-related telephone survey of patients with diabetes. The task is to predict the physical condition of the patients. * CIC_ISCX ([[https://www.unb.ca/cic/datasets/index.html](https://www.unb.ca/cic/datasets/index.html)]([https://www.unb.ca/cic/datasets/index.html](https://www.unb.ca/cic/datasets/index.html)) (accessed on 17 March 2024): This dataset is a network intrusion detection dataset obtained from the Kaggle platform, created by the Canadian Institute for Cybersecurity (CIC). It is used to identify whether each network traffic instance belongs to a certain type of attack or is normal traffic. There are various commonly used evaluation metrics in the classification of multiclass imbalanced data streams. This paper adopts recall, accuracy, G-mean, F1-score, and kappa as evaluation metrics. In binary classification, recall focuses on the prediction performance