## 2 Methods
### KNN-Is and SMOTE in Big Data
Our results show that the preprocessed dataset achieved better or comparable AUC and G-mean results compared to those obtained from the original feature space, despite having significantly fewer dimensions--often less than 10% of the original. This substantial reduction in dimensionality enhances the efficiency of other preprocessing techniques and improves overall computational performance and resource utilization. This study utilized pre-selected fractional distances and a specified number of randomly selected instances for R. Determining the optimal fractional distance and the appropriate number of instances for transforming the feature space into the dissimilarity space remains largely dataset-dependent. Despite the successful results, this study has several limitations: (i) a comparative study with other techniques proposed in the literature, (ii) the datasets used in this research were limited to nine specific datasets, (iii) the number of attributes in the datasets, and (iv) the use of a single classifier. Future research should aim to optimize prototype selection for the dissimilarity transformation further and explore various fractional distances to assess the robustness and adaptability of the proposed method across a broader range of datasets and classification challenges. Conceptualization, A.B., V.G., R.A., and R.F.-J. ; methodology, J.S.S. ; software, A.B. ; validation, A.B., V.G., and J.S.S. ; formal analysis, A.B., V.G., R.A., and R.F.-J. ; investigation, A.B. ; resources, A.B. ; data curation, A.B. and R.A.; writing--original draft preparation, A.B. ; writing--review and editing, V.G., R.A., R.F.-J., and J.S.S. All authors have read and agreed to the published version of the manuscript. This research received Google Cloud credits from the Google for Education program. Not applicable. The data that support the findings of this study are available from the second author, [PERSON], upon reasonable request. The authors declare no conflict of interest. ## References

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\begin{tabular}{c c c c} \hline \hline \(i\) & **Algorithm** & \(z=(R_{0}-R_{1})/SE\) & \(p\) \\ \hline
3 & \(D_{d_{p}}\) & 4.016632 & 0.000059 \\
2 & Imbalanced & 3.651484 & 0.000261 \\
1 & SMOTE & 1.460593 & 0.144127 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Post hoc comparison with \(\alpha=0.05\) and \(D_{d_{p}}\)+SMOTE as algorithm control. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON] Perspectives on Big Data and Big Data Analytics in Healthcare. _Perspect. Health Inf. Manag._**2024**, _21_, 43. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine learning on big data: Opportunities and challenges. _Neurocomputing_**2017**, _237_, 350-361. [CrossRef]
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