# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
After finishing the task, each participant was asked to answer a multiple choice questionnaire, with the questions shown in Table 12, and to justify the grade in a few sentences. For each question the answers 0, 1, 2, 3 and 4 were available, where 0 is the worst score and 4 is the best score. The grades had the following meanings: 0--No; 1--Little; 2--Fair; 3--Good; 4--Excellent. Table 13 presents the means of the grades given to the questions. These results indicate that MILTree provides effective support to MIL by the subjects in the case study. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Cat3**} \\ \hline  & **External** & **Internal** & **Combined** \\  & **Bags** & **Bags** & **Bags** \\ \hline Matching Bags & 104 (68\%) & 134 (73.1\%) & 137 (89.5\%) \\ Non-Matching Bags & 49(32\%) & 19 (26.9\%) & 16 (10.5\%) \\ Accuracy & 72.16\% & 88.31\% & 90.06\% \\ Precision & 71.21\% & 87.74\% & 89.58\% \\ Recall & 67.97\% & 87.58\% & 89.54\% \\ \hline  & \multicolumn{3}{c}{**Cat6**} \\ \hline  & **External** & **Internal** & **Combined** \\  & **Bags** & **Bags** & **Bags** \\ \hline Matching Bags & 134 (85.9\%) & 114 (73.1\%) & 139 (89.1\%) \\ Non-Matching Bags & 22(14.1\%) & 42 (26.9\%) & 17 (10.9\%) \\ Accuracy & 86.82\% & 76.28\% & 89.66\% \\ Precision & 85.93\% & 73.07\% & 89.28\% \\ Recall & 85.9\% & 73.08\% & 89.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of multi-instance classification using three types of training set. \begin{table}
\begin{tabular}{c l} \hline \hline
**No.** & **Questions** \\ \hline
1 & Is it possible to clearly identify the classes using MILTree? \\
2 & Does the browsing through MILTree (Bags and Instances projection space) produce the user to better understand the structure of multiple-instance data? \\
3 & Is the Prototypes ClassMatch tree useful for identifying misclassified bags? \\
4 & Is the ClassMatch tree useful for discovering new bags that help to improve or update the model? \\
5 & Do you feel that MILTree provides useful support in the multiple-instance classification process? \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The questionnaire used in the evaluation. All participants agreed that MILTree provided a good understanding of the multiple-instance data structure and supported them in the classification task. They had some comments as follows: \"_Identifying the classes within dataset is very simple because, generally, instances of the same class are closer_\", \"_In two simple steps it is possible to identify and update misclassified bags using Prototypes-ClassMatch tree_\"; \"_ClassMatch tree is useful because we can identify new bags that could be more representative for each class_\". About MILTree as a tool for multi-instance classification, the majority of participants said that it is easy to use, leaving comments such as: \"_It is a very useful tool for some tasks with multiple-instance data, such as selection of training data, classification and updating of the training data. In these tasks, the tool is easy to use, differently of some other approaches that not use visualization of multi-instance data_\". On the less positive side, two participants recommended that the browsing through of MILTree should be improved, leaving comments such as: \"_In some functionalities, you have to do many clicks to see the results. In some cases it is difficult to see what data (training data or misclassified bags) is being visualized_\"; \"_Provides additional information, maybe as a tooltip, indicating more information about instances in the instances projection space like type, instance_'s preview, etc. It would be excellent_\". All participants successfully finished the user study within approximately 35 min. The majority of participants updated the prototypes of misclassified bags using the bags projection space. Only one explicitly used the instance space for this purpose.