# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 3 Related Work  
Each change detection algorithm has its advantages and drawbacks, and the choice of a particular one will depend on the specific characteristics of the data stream and the requirements of the application [2; 13; 14]. In the context of data stream mining, monitoring the statistical properties of the data stream is one of the most commonly used strategies to detect and identify different types of concept drift. However, there is no single algorithm that works well for all types of drift, and different strategies may be more appropriate for different types of drift. For example, Adaptive Windowing (ADWIN), based on monitoring the mean and variance of the data stream, can be effective for detecting abrupt drift, where the statistical properties of the data change suddenly [10]. On the other hand, monitoring the distribution of the data stream can be more effective for detecting a gradual drift, where the statistical properties of the data change slowly over time, such as the early drift detection method (DDM) [13]. Thus, there are various types of related research on drift detection [24; 25; 26] with different biases and contributions. [PERSON] et al. [27] assessed the reliability of concept drift detectors by exploring how late they were in reporting drifts and how many false alarms they signaled. The study compared the performance of the most popular drift detectors belonging to two different groups, error rate-based detectors and data distribution-based detectors, on both synthetic and real-world data, providing an insightful discussion of the methods. However, the study only covered abrupt and gradual drift, excluding incremental drift. As incremental changes are prevalent in real-world applications, especially in streaming data scenarios, their absence in the study opens the door for further investigation. Another point to consider is that one of the metrics explored to evaluate the drift detectors was latency, which ranges between 0 and 1 to show how late the detector manages to detect the drift. Using latency as a metric to evaluate drift detectors might not provide a comprehensive analysis of the detector's delay performance. The use of latency as a metric can also overlook false alarms generated by drift detectors, as the metric only considers the time until the first alarm is raised for a given drift. Additionally, latency might not be appropriate for evaluating some types of drift detectors, such as those based on sliding window techniques. The detectorsthat operate on a sliding window of data are grounded on detecting the drift as soon as it occurs, regardless of how long it takes to make a prediction on the data within the window. [PERSON] et al. [28] evaluated the performance of eight distinct concept drift detectors using artificial datasets that were subject to both abrupt and gradual concept drifts, as well as real-world datasets. The experiments compared the accuracy, evaluation time as well as false alarms and miss detection rates. As with [PERSON] et al. [27], incremental change was not considered as a type of drift to be explored. However, an important metric was used to enable a fair comparison among drift detectors--the distance to the drift point, which provides information on which method most accurately identified the position of the drift. Considering the different applications of drift detectors, [PERSON] and [PERSON] [24] compared their configurations of the concept drift detectors in artificial datasets. The main goal was to adequately measure the performance of concept drift detectors and pinpoint the common challenges in detecting drifts closer to their correct positions. Furthermore, the study aimed to determine the parameters that had the most significant impact on the predictive accuracy of the methods. Following the previous surveys, the authors only considered abrupt and gradual changes, limiting the scope of the experiments. In summary, the reviewed studies explore similar contexts and types of drift, with some variation in the metrics used to analyze performance.