# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 5 Insight to When to Amalgamate DC-AI with MC-AI
### Limited Availability of Representative Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ML Algorithms**} & \multicolumn{2}{c}{\(\mathcal{A}\)**Results under Three Distinct Settings (Mostly MC-AI)**} \\ \cline{2-4}  & \(\mathbf{\alpha}\) & \(\beta\) & \(\gamma\) \\ \hline Decision tree & 0.7886\(\sim\)0.8291 & 0.7986\(\sim\)0.8123 & \(\sim\)83.31 \\ SVM & 0.8186\(\sim\)0.8491 & 0.8281\(\sim\)0.8501 & \(\sim\)86.79 \\ Random forest & 0.8286\(\sim\)0.8798 & 0.8481\(\sim\)0.8929 & \(\sim\)92.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(\mathcal{A}\) comparisons of using the MC-AI approach (\(\alpha\) = (data size change), \(\beta\) = (hyperparameter tuning), and \(\gamma\) = (data size and hyperparameter tuning)). Figure 7: Structure of confusion matrix. This schematic shows the structure of the confusion matrix obtained from experiments via MC-AI. problems via DC-AI. In this pipeline, there is a total of six steps that were sequentially applied to curate better data for enhancing the performance of the AI/ML models. In this dataset, the number of records is very low and the distribution skew is also very high. Therefore, after identifying these two vulnerabilities in the dataset, we curated some more data with the help of a conditional generative adversarial network (CGAN [66]). Afterward, the data fusion was performed in the minority class to enhance the performance of the ML classifiers. Although many data augmentation techniques have been proposed to address the class imbalance problem, our approach is different as it adds fewer, but good-quality records, leading to significant enhancement in the results. Specifically, we added only 3042 records in the stoke prediction dataset by rigorously following the DC-AI criteria. In this case, the existing techniques that do not follow DC-AI may add 5366 records [67]. The addition of more records by not following DC-AI can lead to only very small improvements in the results, whereas the computing overheads can be large, especially when the data size is large. In contrast, the careful application of DC-AI can significantly improve the results without increasing the computing cost. To evaluate the significance of DC-AI, we applied three different settings similar to the MC-AI approach. In setting I, we fixed the parameters of the ML models, but varied the number of records in the testing and training data. In this case, we added more records in the original data step by step and built the model. In the second setting, we froze the training and testing data, but varied the hyperparameters of the ML algorithms. In the third setting, we reduced the hyperparameters to prove the benefits of DC-AI. In this setting, we built the model with fewer parameters while using good-quality data. For example, in a random forest, we reduced the number of trees to be constructed. Similarly, in SVM, we reduced the parameter size using the grid-search method. In the experiments, we found that if the data quality is good, the hyperparameters can be reduced (or small values can be used), leading to improved results. For the sake of simplicity, we name these settings \(\alpha\), \(\beta\), and \(\gamma\). Table 3 presents the results of the experiment that were obtained from improved data. From the results, it can be seen that DC-AI yielded a much higher performance compared to MC-AI, and the \(\mathcal{A}\) value is close to 100% in the random forest case. Furthermore, it is worth noting that this value was attained with significantly reduced hyperparameters compared to MC-AI. These results prove the supremacy of DC-AI over MC-AI. The DC-AI approach has generated very balanced confusion matrices compared to the MC-AI approach. Specifically, we observed that, in most experiments, \(T_{p}>F_{p}\) and  
Figure 8: DC-AI pipeline applied to stroke prediction data. This schematic shows the process used to significantly enhance the data quality.