# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
To find the _InstancePrototypes ClassMatch_ tree for this dataset, we allow users to select a training set to create an SVM classifier. Figure 5b shows the training set that was used to create the classifier; dark red bags are the ones used for training, while the pale blue ones are used as validation/test. Finally, the _InstancePrototypes ClassMatch_ tree shows the classification results (see Figure 5c), where dark red points are misclassified bags, probably with non-representative prototypes. Updating the prototypes will improve the model, as indicated by the results presented later in Section 6. \[c_{i,j}=D_{i,j}-\frac{r_{i}+r_{j}}{-2}, \tag{5}\]0 Methods for selecting instance prototypes \(B_{ix}\) and \(B_{iy}\). Both (**a**) and (**b**) project the same instances from a positive bag \(B_{i}^{+}\) of the MUSK1 dataset on the MILTree’s instance space layout. \[c_{i,j}=D_{i,j}-\frac{r_{i}+r_{j}}{-2}, \tag{5}\]1 Selection of instance prototype using MILTree-Med. ## 5 Application of MILTree to Multiple-Instance Learning Scenarios

In this section, we present three case studies that illustrate the practical usefulness of MILTree. The case studies were carried out on a Dell workstation Z620 equipped with an Intel Core CPU (E5-2690, 3.40 GHz) and 16 GB memory. The first presents a binary multiple-instance image classification problem using the Corel People dataset. The second one describes a multi-class scenario using images from five classes of the Corel dataset. Lastly, the MIL benchmark dataset Musk1 is used in the third case study. More information about each dataset can be found in Section 6. ### Case 1: Instance Space Layout in a Binary Classification Problem

We demonstrate that an appropriate selection of instance prototypes can influence the accuracy of the classification. We use MILTree for the layout and MILTree-Med as instance prototype selection method, and the updates of prototypes are performed in the Instance Space Layout. For this case, we use the Corel People binary dataset with 200 bags (images) and 938 instances (feature vectors extracted from image regions), which includes 100 images from the class People (positive) and 100 images randomly selected from all other classes (negative) of the Corel-1000 dataset. Figure 5(a) shows the projection of the bags using MILTree: red bags represent positive bags (images of people) and blue bags represent negative bags (images from other categories). The first step in the classification process is to select the training set: 20% of the images are selected to train the model, while the remaining 80% is used for validation and test. Due to the nature of the NJ algorithm, MILTree positions the bags that better characterize the class they belong to as far as possible from the core of the tree (external points), while the bags located in the core of the tree (internal points) have features that overlap with other classes. This characteristic can be extremely useful in identifying a representative sample by selecting both external and internal bags to create our training set so as to build a classifier that is neither too restrictive nor too general. This visual selection strategy

\[c_{i,j}=D_{i,j}-\frac{r_{i}+r_{j}}{-2}, \tag{5}\]2 MILTree’s bag space layout for a subset of the Corel-1000 dataset (100 images of the horse category and 100 random images selected from the remaining categories), with the projection of its ground truth (**a**), selected training set (**b**) and _InstancePrototypes ClassMatch_ tree (**c**). \[c_{i,j}=D_{i,j}-\frac{r_{i}+r_{j}}{-2}, \tag{5}\]3 MILTree’s Bag Space Layout for the People Category of the Corel-1000 dataset, with the projection of its Ground truth (**a**), selected training set (**b**) and _InstancePrototypes ClassMatch tree_ (**c**). has been demonstrated to produce better results in standard supervised learning when compared with random selection strategies [21]. Note that, in our representation, when the user selects a bag she/he is actually selecting its instance prototype \(B_{\textit{protoClass}}\).