# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)

Table 8: Comparison between MILTree-SI / MILTree-Med and related methods from the literature on the 1000-Corel and 2000-Corel Datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Bags** & **Instances** & **Dimensions** \\ \hline Components & 423 & 9104 & 200 \\ Functions & 443 & 9387 & 200 \\ Processes & 757 & 25,181 & 200 \\ \hline Total & 1623 & 34,569 & 600 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Biocreative dataset. Total number of bags and instances for each category. ### MILTree Layout Bag Positioning

This experiment aims at evaluating how the bag positions in the MILTree layout are related to good candidates for training set selection. As mentioned in Section 5.1, the MILTree projects a bag belonging to a given class as an external point of the tree if it is furthest from the remaining classes. At the core of the MILTree (internal points) will be the bags that are closest to other classes, as well as the ones that overlap in feature space. We followed a similar methodology to investigate the impact of bag positioning on the classification results. Three training sets are used in this analysis; the first training set is composed only of external instances, the second training set is composed only of internal instances and the third training set is composed of a combination of the first and second training sets. The Corel's Cat3 and Cat6 subdatasets and MILTree-Med were used for this experiment. For Cat3, a total of 47 training bags are selected as training examples, while the remaining 153 bags are used as test set. For the Cat6, 44 training bags were selected for training, while the remaining 156 bags are used as the test set. Table 11 shows the results of each multi-instance classification for both collections. When using just external points, the model is often unable to represent boundary elements, resulting in a classifier that does not take into account the overlap degree between the classes. Using only internal bags, we add this information in the training set, but by combining external and internal bags, we have a sample containing both the more class-distinct elements and the ones belonging to the decision boundary region, resulting in a more accurate classifier. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Biocreative** \\ \hline MILTree-Med & **99.1** \\ MILTree-SI & 96.3 \\ \hline MI-SVM & 90.9 \\ EM-DD & 91.0 \\ DD & 90.9 \\ MIWrapper & 90.5 \\ TLDSimple & 85.0 \\ MIBoost & 90.5 \\ \hline \hline \end{tabular} Bold value indicates the method that obtains the best performance.

\end{table} 
Table 10: Comparison of classification accuracy between MILTree-SI/MILTree-Med and baseline methods on the Biocreative dataset. Figure 13: Bag space projection of MILTree for the Biocreative dataset using MILTree-SI, with the projection of its _ground truth_ (**a**) and the selected training data (red bags) (**b**). ## 7 User Study

In this section, we present a user study to evaluate the usability of the MILTree layout for multiple-instance learning problems. We conducted the user study with five participants, three male and two female, who were all undergraduate or graduate students. The age of the participants ranged from 20 to 33. All participants, except one who received additional guidance, had previous knowledge about supervised learning and classification models. All users performed the same task, which was to build a multiple-instance model for the Corel-300 dataset (see Section 5.2 for details). We prepared two 10-minute long videos to instruct how to use MILTree for multiple-instance classification where we used datasets other than Corel-300 as examples. All participants watched the training videos prior to starting the study. We also introduced MILTree to all participants and showed how they could use it. All participants used MILTree-SI as the instance prototype selection method and were instructed to use around 20% of the data to train the model, while the remaining 80% would be used for validation and testing.