# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
The final accuracy achieved by the participants on the Corel-300 dataset is presented in Table 14. As we can see, the average is 86.16%, which is comparable to the 83.8% achieved in Section 5.2. This is because most participants selected a similar training set, learning from the instructions that it was possible to obtain good classification models by choosing bags located both on leaves and the core of the tree (see Section 6.5). ## 8 Statistical Analysis

The statistical analysis was carried out using the main MIL benchmark datasets: Corel-1000, Corel-2000, Musk1, Musk2, Elephant, Fox and Tiger. The statistical non-parametric test proposed by [43] was used to compare the results across the most relevant competing methods, which produces a ranking, as shown in Table 15, a contrast estimation table between the methods, as shown in Table 16, and post hoc tests as shown in Table 17, in order to verify which methods are significantly different from the best method, i.e., the top-ranked one. Our MILTree-Med was considered by the Friedman test to be the first in the ranking (see Table 15) with \(p\leq 0.01\); this is confirmed by the contrast estimation table, showing that it has higher (positive) average accuracy when compared with all other methods (see Table 16). According to the Holm's post hoc procedure (see Table 17), the MILTree \begin{table}
\begin{tabular}{c c} \hline \hline
**Questions** & **Grade** \\ \hline Question 1 & 3.2 \\ Question 2 & 2.8 \\ Question 3 & 3 \\ Question 4 & 2.4 \\ Question 5 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Means of the results obtained in the evaluation using the questionnaire for multi-instance classification of Corel-300 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Participants**} \\ \cline{2-7}
**Task** & **User1** & **User2** & **User3** & **User4** & **User5** & **Average** \\ \hline Corel-300 & 85.87 & 87.28 & 85.47 & 87.42 & 84.75 & **86.16** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of multi-instance classification obtained by each participants on Corel-300 dataset. Med accuracy is significantly better than MI-SVM, EMDD, mi-SVM and DD-SVM, but it is not different when compared with MILES, MILDE and MILTree-SI. [PERSON]'s post hoc procedure found that only MILDE is comparable with our MILTree-Med approach, while the remaining ones present statistically lower accuracies (see Table 17). These results corroborate our findings, encouraging the use of visual tools to support different MIL tasks. ## 9 Conclusions

In this paper, we propose MILTree for visual data mining in multiple-instance learning scenarios using an intuitive two-level tree structure that resembles MIL data models. While visually supporting data understanding, our approach also handles multiclass problems: the MILTree-SI selection method aims to uncover the most representative instances in both positive and negative bags, where negative bags could also have positive instances; the MILTree-Med method uses a clustering algorithm to partition unlabeled instances in search of positive and negative clusters to identify adequate instance prototypes.