# Process-Oriented Stream Classification Pipeline: A Literature Review
## 7 Current and Future Research Directions
### Current Work in the Field
#### 7.2.4 Classifier Maintenance  
As depicted in Figure 10, a large number of research papers of the last two years include the topic of \"concept drift\", \"detection\", or the appearance of \"novel\" classes. For example, a state-of-the-art approach to finding novel classes is to use neighborhood-based approaches [197]. Concretely, they focus on the cohesiveness and separation index of the Mahalanobis distance. Similarly, ref. [166] base their emerging class detection and classification on microclusters. All data instances not immediately assigned to a close microcluster are clustered with a \(k\)-means algorithm, and if the clusters are pure, a novel class is detected. Ref. [198] introduces a density-based clustering approach where they monitor sub-cluster regions to detect and adapt to concept drifts and detect novel classes. Moreover, words related to classifier assessment such as \"performance\" and \"accuracy\", as well as \"evaluation\", can be observed. Assessing the performance of stream classification models is a multi-dimensional problem requiring many steps. Some are already adjusted to the requirements of stream classification, while others still use methods derived from the static setting. First, the training and testing data have to be determined. Prequential evaluation and controlled permutations are state-of-the-art methods for the stream setting, as can be observed in the recent comparative works of [7,88], since they take the volatile nature of stream classification data into account. In contrast, most works on metrics focus on time-tested measurements derived from the static setting. For example, measures such as accuracy, precision, and recall are used in recent studies, as can be observed in the comparative work of [30,107,199,200]. However,this kind of metric does not reflect the volatile nature of stream classification data and algorithms. While there are metrics that have been adapted for the stream classification scenario--such as the \(\kappa\) statistics [83; 169], no further progress has been made recently. Nevertheless, other relevant metrics such as the duration of all preprocessing steps, RAM hours, or memory usage are used in recent works to assess different dimensions of the stream classifier's performance [88]. However, a unified metric or concept is still missing. The data's time dependency, as well as possible verification latencies, complicate conventional assessment methods. Particularly in the case of problems that complicate the standard classification of data streams--e.g., the emergence of novel classes, multiple label classification, or highly imbalanced classes--appropriate measurement frameworks are still missing, as pointed out in the works of [7; 86]. The same issue applies to statistical tests for comparing classifier performances. These tests were developed for comparing classifier results in static settings and are not for stream-specific requirements. Although they are not included in recent surveys, e.g., by [88] or [7], they are still being used in novel studies, such as, for example, by [201; 202]. Since they are not developed to test for the variable stream setting and take more than one dimension of the problem into account, they only provide a very limited view of the classifier's performance. Ensemble evaluation methods also mostly rely on batch processing metrics, although advancements for stream purposes are being made. Ref. [179] introduced two new trend diversity measures based on the direction and magnitude of changes in the error trends of classifiers. This measure is used in recent studies, e.g., by [203; 204]. Finally, the stream classifier and the drift detection algorithm must be evaluated. Ideally, the algorithm should indicate a data distribution shift at the right point in time in the right direction. A standard drift detection method that is being used in recent studies is, e.g., the False Alarm rate [205]--however, a comparative study as a recommendation on when to use which metric was not conducted until now.