## 3 Improving Multi-Class Classification for Recognition of the Prioritized Classes Using the AHP  
In this section, we present and explain the main features of the proposed method. A reliable method for evaluating model performance is essential in several areas of adaptable or customizable ML. Some solutions involving federated learning use model performance metrics to decide if the newly received model updates are desirable in terms of the overall global model improvement or not. Authors [27] use performance evaluation to distinguish malicious members of the federation and to eliminate potentially harmful model updates from the global model. A similar approach is also valid for transfer learning, when the initial model is additionally trained using a local dataset to achieve a better-behaving final model suitable for the very specific custom requirements of the final user. Incremental learning is an ML method that allows models, like deep learning models, to keep learning new information continuously by analyzing changing data they receive from a data stream [28]. Through the use of incremental learning, it is possible to develop AI systems that are capable of continuously updating themselves to include new information while preserving the information they already possess. Adaptive training is orchestrated based on performance metrics in the specific field only relevant for the concrete customer. Adaptive incremental learning also requires reliable metrics of the model's performance to decide if it is desirable to include the newly acquired data in the model. If the two-class classification is used, then classical composite scores, such as accuracy, precision, recall, and F1-score, may be used as model performance metrics. The situation dramatically changes when multi-class classification is used. In such a scenario, precision, recall and F1-score are calculated for each class individually, and it is quite common when a single characteristic increases for one class and decreases for the other class. The problem arises from the natural weaknesses of the combined scores, which often lead to confusing results when trying to improve and assess prediction models. In such cases, it is very difficult to decide whether the potential changes to the model are desirable or not. To mitigate the shortcomings of the composite scores, we propose using the AHP [29; 30] to evaluate the performance of the model and make the decision. The proposed method workflow is depicted in Figure 1.  
The main steps of the proposed incremental learning process are as follows:  
1. Use the initial available data and train the incremental ML model. 2. As a result of the first training, the initial model is produced, and initial performance characteristics are calculated. The initial model is marked as the current model. 3. After the new batch of training data is available, train the current version of the ML model using the new data. 4. As a result of step 3, the new ML model is produced, and performance characteristics are obtained. Figure 1: The AHP is utilized in the adaptive incremental ML process. Present the performance metrics of both the existing model and the new model for the AHP decision-making process. * Load the application-specific AHP judgment matrix with the results of the pairwise comparison of the metrics receiving prioritization;
* Use the AHP process and make decisions to include (or not to include) the new data in the prioritized model. If the decision is to not include the new data, then the new ML model is discarded. Otherwise, the new ML model is marked as the current model. * Repeat the process starting, from step 3, until no new data is available. The current ML model becomes the final updated prioritized model after processing all batches of data. The AHP enables judgments to be made based on pairwise comparisons of many criteria that are carried out by specialists in the subject.