# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **Dataset** & **Citation-KNN** & **MIBoost** & **MIEMDD** & **MILR** & **MISVM** & **MITI** & **MIPART** \\ \hline
1 & Musk1 & 83.70 & 51.09 & 86.96 & 72.83 & 75.00 & 65.22 & 86.96 \\
2 & Musk2 & 73.53 & 61.76 & 85.29 & 82.35 & 74.51 & 71.57 & 86.27 \\
3 & Elephant & 50.00 & 50.00 & 77.00 & 79.00 & 78.00 & 79.00 & 86.50 \\
4 & Fox & 50.00 & 50.00 & 59.50 & 57.50 & 47.00 & 61.00 & 64.50 \\
5 & Tiger & 50.00 & 50.00 & 75.00 & 78.00 & 82.50 & 75.50 & 82.50 \\
6 & mutagenesis3\_atoms & 76.06 & 66.49 & 71.28 & 72.34 & 66.49 & 81.38 & 81.38 \\
7 & mutagenesis3\_bonds & 72.87 & 66.49 & 74.47 & 75.53 & 66.49 & 81.38 & 87.77 \\
8 & mutagenesis3\_chains & 76.60 & 66.49 & 71.28 & 78.19 & 66.49 & 84.57 & 85.11 \\ \hline
9 & component & 86.49 & 86.49 & 84.92 & 90.22 & 86.90 & 88.66 & 93.58 \\ \hline
10 & eastwest & 40.00 & 50.00 & 50.00 & 65.00 & 60.00 & 50.00 & 80.00 \\ \hline
11 & function & 91.55 & 91.55 & 91.81 & 93.74 & 91.82 & 94.45 & 96.70 \\ \hline
12 & westeast & 50.00 & 50.00 & 25.00 & 50.00 & 30.00 & 35.00 & 80.00 \\ \hline  & **Average** & 66.73 & 61.70 & 71.04 & 74.56 & 68.77 & 72.31 & **84.27** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of MIPART with different multi-instance classifiers in accuracy metric (%). mutagenesis3_bonds, mutagenesis3_chains, eastwest, and westeast datasets range between 0.80 and 0.88, while the Fox dataset has the lowest sensitivity at 0.65. The sensitivity or recall metric complements the precision-recall curve (PRC) area by directly measuring the proportion of actual positive cases correctly identified by the model. For imbalanced datasets, sensitivity ensures the model's capability to prioritize minority class detection, which is particularly important for validating the model's performance on such datasets. As shown in Figure 4, our method achieved an average sensitivity of 0.84, demonstrating its consistent performance in capturing minority-class instances across the datasets. Beyond standard metrics like accuracy, sensitivity, and precision, the PRC area metric provides an additional measure of the MIPART method's performance on twelve multi-instance datasets, as shown in Figure 5. The highest PRC area value of 0.98 was achieved with the function dataset, followed by the component dataset at 0.97. The PRC area values for the Musk1, Tiger, Elephant, mutagenesis3_bonds, and mutagenesis3_chains datasets range from 0.90 to 0.94, demonstrating the MIPART method's effectiveness in handling multi-instance classification tasks. The PRC area values for the Musk2, mutagenesis3_atoms, eastwest, and westeast datasets also remain high, between 0.84 and 0.87, while the Fox dataset exhibited the lowest PRC area. Similar to the sensitivity metric, PRC area is especially relevant for imbalanced datasets as it focuses on the trade-off between precision and recall, emphasizing the accurate detection of minority class instances without being skewed by the majority class. Its high average value of 0.89, as illustrated in Figure 5, confirms the model's ability to maintain a balance between these two metrics, ensuring reliable detection of minority class instances across 12 multi-label datasets. This makes the PRC area a critical metric for evaluating the performance of models, particularly in scenarios where class imbalance is a significant concern. Figure 4: Sensitivity results of the MIPART method across twelve multi-instance datasets. To further assess the MIPART method's performance, the ROC area metric was evaluated across twelve multi-instance learning datasets, as indicated in Figure 6. The receiver operating characteristic (ROC) curve provides a graphical representation of the true positive rate (TPR) versus the false positive rate (FPR) across various classification thresholds.