## 4 Experimental Results
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Type** & \begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & **Features** & **Dataset Size** & **Use Case** & **Relevance for Unlearning** \\ \hline \multirow{4}{*}{CIFAR-10 [18]} & \multirow{4}{*}{\begin{tabular}{c} Image \\ Classification \\ \end{tabular} } & \multirow{4}{*}{60,000} & \(32\times 32\) color & \multirow{4}{*}{\(\sim\)163 MB} & CIFAR-10 is more & CIFAR-10 is ideal for \\  & & & & complex & testing the efficiency of \\  & & & & compared to & ontology-based \\  & & & images (3072 \\  & & features) & & & MNIST, with & unlearning, Ontology can \\  & & & features) & & color images and & help prioritize images \\  & & & & multiple & based on their impact on \\  & & & & categories. & classification accuracy. \\ \hline \multirow{4}{*}{UCI Adult Census [19]} & \multirow{4}{*}{Tabular Data} & \multirow{4}{*}{48,842} & \multirow{4}{*}{14 features (age, education, etc.)} & \multirow{4}{*}{\(\sim\)4 MB} & The dataset is & This dataset allows us to \\  & & & typically used for & evaluate how both \\  & & & income & algorithms handle & \\  & & & classification & unlearning across \\  & & & & tasks and serves & different data types, \\  & & & & as a benchmark & including numerical and \\  & & & & for machine & categorical features. \\  & & & & learning & Ontology can prioritize \\  & & & & algorithms & the unlearning of sensitive \\  & & & & dealing with & features (e.g., gender \\  & & & & tabular data. & and race). \\ \hline \multirow{4}{*}{Purchase [20]} & \multirow{4}{*}{\begin{tabular}{c} Transactional \\ Data \\ \end{tabular} } & \multirow{4}{*}{197,324} & \multirow{4}{*}{\begin{tabular}{c} Product ID, \\ category, store, \\ transaction \\ \end{tabular} } & \multirow{4}{*}{\(\sim\)20 MB} & \multirow{4}{*}{\begin{tabular}{c} The dataset is \\ data used in \\ recommendation \\ systems and \\ analysis to derive \\ customer buying \\ patterns. \\ \end{tabular} } & The dataset is highly \\  & & & & suitable for evaluating the \\  & & & & & ability of unlearning \\  & & & & often used in & algorithms to handle \\  & & & recommendation & & \\  & & & systems and & & \\  & & & & market basket & \\  & & & analysis to derive & \\  & & & & analysis to derive & \\  & & & customer buying & & \\  & & & patterns. & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{MIMIC-III [21]} & \multirow{4}{*}{\begin{tabular}{c} Medical \\ Diagnosis \\ admissions \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Medical \\ hospital \\ admissions \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Patient \\ demographics, \\ vitals, \\ medications \\ \end{tabular} } & \multirow{4}{*}{\(\sim\)60 GB} & \multirow{4}{*}{\begin{tabular}{c} MIMIC-III is \\ widely used for \\ predictive \\ modeling in \\ healthcare, such \\ as predicting \\ mortality, length \\ of stay, and \\ treatment \\ outcomes. \\ \end{tabular} } & \multicolumn{4}{c}{\begin{tabular}{c} MIMIC-III is \\ which \\ this context helps to assess \\ how well OFU-Ontology \\ can prioritize unlearning \\ of less critical clinical \\ information while \\ retaining essential \\ features that significantly \\ affect the model’s \\ predictions. \\ \end{tabular} } \\ \hline \multirow{4}{*}{\begin{tabular}{c} KDD Cup \\ 1999 [22] \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Network \\ Security Data \\ \end{tabular} } & \multirow{4}{*}{4,898,431} & 41 features (protocol type,  service, etc.)} & \multirow{4}{*}{-743 MB} & The dataset is & This dataset allows us to \\  & & & used for network & assess how efficiently the \\  & & & & & algorithms can handle \\  & & & & detection tasks to \\  & & & & classify network & that the model retains the \\  & & & & traffic as normal & ability to accurately detect \\  & & & service, etc.) & & intrusions. \\  & & & & & intrusions. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & features. & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Reddit \\ Comment [23] \\ \end{tabular} } & \multirow{4}{*}{Text Data & \(\sim\)100 million comments} & \multirow{4}{*}{
\begin{tabular}{c} Comment text, \\ metadata (author, \\ subreddit) \\ \end{tabular} } & \multirow{4}{*}{\(\sim\)50 GB} & This dataset is & The Reddit Comment & dataset is suitable for \\  & & & & sentiment & \\  & & & metadata (author, \\  & & & subreddit) & \(\sim\)50 GB & analysis, topic \\  & & & modeling, or text & & \\  & & & classification & & \\  & & & & tasks. & \\ \hline \hline \end{tabular}
\end{table}