## 5 Discussion  
Previous research in the field of classification techniques indicates that the efficacy of these methods is significantly influenced by the characteristics of the dataset [37].  
A prevalent issue impacting raw data is the class imbalance problem, which denotes an unequal distribution of values in the response variable. In contrast, there is an additional problem that arises when a machine learning model focuses on a subset of classes rather than all the potential classes. This issue arises when the model is only capable of classifying specific classes. This occurs when using binary classification rather than classifying prioritized classes. The problems mention exist in various fields, including CTL, Fintech fraud detection, IDS, and various other domains where negatively labeled instances substantially exceed favorably labeled instances.  
One of the primary goals of ML algorithms is to achieve the highest possible level of accuracy over the whole dataset. This results in the majority class samples receiving a greater amount of attention. Because of this, the learning model does not generate accurate predictions for the minority class samples [38]. To resolve imbalance issues, data-driven and algorithm-driven methods are frequently used.  
Data-driven methods make adjustments to the class ratio in the input dataset to produce a balanced distribution of classes. This strategy frequently makes use of sampling methods, such as undersampling, oversampling, or a mix of the two aforementioned methods.  
Using algorithm-driven methods, the classification algorithm is modified to make the learning process more manageable, particularly with regard to the minority class. This method does not include any modifications to the distribution of the data that is being input. This strategy incorporates various other approaches, such as thresholding, cost-sensitive learning, and hybrid methods such as ensemble learners [38].  
The experimental results were compared to state-of-the-art (SOTA) research, as presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Important Attack Classes** & **Step** & **Recall** & **Precision** & **F1-Score** \\
**That Have Been Prioritized** & **Step** & **Recall** & **Precision** & **F1-Score** \\ \hline Injection & 1 & 0.317 & 0.830 & 0.459 \\  & 37 & 0.618 & 0.892 & 0.730 \\ \hline Password & 1 & 0.757 & 0.734 & 0.745 \\  & 37 & 0.886 & 0.800 & 0.840 \\ \hline  & 1 & 0.000 & 0.000 & 0.000 \\  & 37 & 0.236 & 0.484 & 0.317 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The recall, precision, and F1-score values of the most prioritized classes from the first and the last incremental learning steps.  
The conclusion that can be drawn from Table 5 is that there are a number of alternative SOTA algorithms that can be utilized to classify unbalanced multi-class data. The proposed approach differs from the others in the following ways:  
* The proposed approach employs incremental learning, AHP, and ECOC to improve ML model performance for the important classes that have been prioritized.
* Unlike data-driven methods, the proposed approach does not involve any modifications to the dataset; rather, we use the original multi-class dataset in its current form.