## 5 Discussion  
Our evaluation findings demonstrate that the configuration of the exploration algorithm's parameters markedly shapes the balance between cost and detection quality. In essence, any attempt to reduce computational expense may risk impairing detection accuracy. Among the parameter selection strategies explored here, the Softmax approach displayed noteworthy adaptability over a broad parameter space, making it particularly suitable for stakeholders whose risk tolerance may vary. Unsurprisingly, our trials indicated that approaches engaging in more extensive triage operations, thus incurring greater computational overheads, attained superior detection metrics. This result aligns with the principle that assembling multiple predictive models, rather than relying on a single model limited to a reduced set of features, enhances overall performance. Nevertheless, as discussed in Section 4, our introduction of the contextual index reward function \(log(k)\) in our contextual bandit-based reinforcement learning approach enables a more economical balance. By adopting \(log(k)\), our contextual bandit-based \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Detection Model** & **Architecture** & **Accuracy** & **Precision** & **Recall** & **Fi-Score** & **Triage Rate** \\ \hline \multirow{8}{*}{Subset A} & \multirow{4}{*}{Neural Network} & Large & 0.91 & 0.92 & 0.88 & 0.9 & N/A \\  & & Stacked & 0.9 & 0.93 & 0.87 & 0.89 & N/A \\  & & Light (L1) & 0.78 & 0.81 & 0.74 & 0.77 & N/A \\  & & Light (L2) & 0.96 & 0.88 & 0.9 & 0.91 & N/A \\ \cline{2-7}  & Contextual Bandit (\(log(k)\), UCB) & Cost-Sensitive & 0.9 & 0.89 & 0.91 & 0.9 & 1650.0 \\ \cline{2-7}  & Contextual Bandit (\(\rho^{d_{\mathrm{T}}\sim d}\), & \multirow{2}{*}{Cost-Sensitive} & \multirow{2}{*}{0.91} & \multirow{2}{*}{0.9} & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.91} & \multirow{2}{*}{3450.0} \\  & Thompson) & & & & & \\ \cline{2-7}  & Random Forest & Large & 0.97 & 0.96 & 0.97 & 0.96 & N/A \\ \hline \multirow{8}{*}{Subset B} & \multirow{4}{*}{Random Forest} & Stacked & 0.96 & 0.96 & 0.95 & 0.95 & N/A \\  & & Light (L1) & 0.89 & 0.87 & 0.86 & 0.86 & N/A \\  & & Light (L2) & 0.96 & 0.94 & 0.95 & 0.95 & N/A \\ \cline{2-7}  & Contextual Bandit (0.9, Thompson) & Cost-Sensitive & 0.92 & 0.9 & 0.93 & 0.91 & 1750.4 \\ \cline{2-7}  & Contextual Bandit (\(log(k)\), UCBe) & Cost-Sensitive & 0.93 & 0.91 & 0.94 & 0.92 & 720.3 \\ \cline{2-7}  & \multirow{4}{*}{Neural Network} & Large & 0.98 & 0.99 & 0.97 & 0.98 & N/A \\  & & Stacked & 0.99 & 0.99 & 0.99 & 0.99 & N/A \\ \cline{2-7}  & & Light (L1) & 0.79 & 0.81 & 0.78 & 0.79 & N/A \\  & & Light (L2) & 0.99 & 0.99 & 0.99 & 0.99 & N/A \\ \hline \multirow{2}{*}{Contextual Bandit (\(log(k)\),  Thompson)} & \multirow{2}{*}{Cost-Sensitive} & \multirow{2}{*}{0.97} & \multirow{2}{*}{0.98} & \multirow{2}{*}{0.96} & \multirow{2}{*}{0.97} & \multirow{2}{*}{5200.38} \\  & Contextual Bandit (\(log(k)\), UCB) & & & & & \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Random Forest} & Large & 0.99 & 0.98 & 0.99 & 0.99 & N/A \\ \cline{1-1} \cline{2-7}  & & Stacked & 0.98 & 0.97 & 0.98 & 0.98 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of detection model architectures on subsets A and B, including traditional and contextual bandit-based models. reinforcement learning approach achieves cost savings while maintaining detection results that are not far removed from those of the optimal architecture described in Table 4. System administrators retain the option to select reward functions that emphasize either computational thrift or enhanced detection rigor, according to their priorities. However, when practical considerations such as human oversight significantly inflate operational costs, the importance of cost trade-offs intensifies. Our comparison of reward policies across various selection algorithms and both classifier/dataset settings revealed that the contextual index reward consistently delivered superior cost reductions in three out of four cases, underscoring its dependability.