# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
Table 7 presents the accuracy of different methods from the literature, including EMDD [23], mi-SVM [11], MI-SVM [11], DD-SVM [37] and SMILES [14]. We can see that our MILTree-SI and MILTree-Med methods achieve high classification accuracy on the sub-datasets. In particular, MILTree-Med outperforms all the others in all but three datasets. The competing methods EMDD, MI-SVM and DD-SVM selects only one instance as a prototype, which is often not sufficient. Moreover, our method was able to be competitive when compared with mi-SVM and SMILES, even though both use all instances from each bag to build the classifier. ### Multiple-Instance Multiclass Datasets

In this section, we turn our attention to the performance of MILTree-Med and MILTree-SI using the MILTree layout for solving multiclass classification problems. The baseline methods, such as EM-DD, mi-SVM, MI-SVM and DD-SVM, were originally proposed for binary class classification. Our MILTree layout and MILTree-Med and MILTree-SI methods also support multiclass datasets. We extend MILTree-Med and MILTree-SI for multiclass by performing one-against-all by decomposing the problem into a number of binary classifiers that are created to separate each class from the remaining ones. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Measures**} \\ \cline{2-9}
**Category ID** & **Inst/Bag** & **Accur** & **Prec** & **Recall** & **F1** & **Proto** & **AddProto** & **AddBags** \\ \hline Category0 & 4.84 & 68.14 & 62.24 & 62.11 & 0.62 & 0 & 11 & 2 \\ Category1 & 3.54 & 75.82 & 74.94 & 72.67 & 0.74 & 3 & 0 & 0 \\ Category2 & 3.1 & 76.47 & 73.33 & 73.33 & 0.73 & 3 & 0 & 2 \\ Category3 & 7.59 & 72.88 & 68.94 & 68.63 & 0.69 & 0 & 12 & 0 \\ Category4 & 2.00 & 83.65 & 84.99 & 82.28 & 0.84 & 4 & 0 & 1 \\ Category5 & 3.02 & 80.86 & 80.84 & 78.95 & 0.8 & 0 & 14 & 0 \\ Category6 & 4.46 & 89.09 & 88.58 & 88.46 & 0.89 & 1 & 0 & 1 \\ Category7 & 3.89 & 80.03 & 78.28 & 77.85 & 0.78 & 0 & 6 & 0 \\ Category8 & 3.38 & 75.21 & 71.7 & 71.7 & 0.72 & 0 & 0 & 2 \\ Category9 & 7.24 & 75.74 & 72.39 & 72.39 & 0.72 & 0 & 6 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results using MILTree-SI on the Corel Dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Measures**} \\ \cline{2-9}
**Category ID** & **Inst/Bag** & **Accur** & **Prec** & **Recall** & **F1** & **Proto** & **AddBags** \\ \hline Category0 & 4.84 & 75.96 & 72.68 & 72.67 & 0.73 & 7 & 0 \\ Category1 & 3.54 & 79.07 & 78.74 & 76.74 & 0.78 & 3 & 2 \\ Category2 & 3.1 & 78.97 & 79.16 & 76.67 & 0.78 & 6 & 3 \\ Category3 & 7.59 & 91.25 & 90.89 & 90.85 & 0.91 & 4 & 0 \\ Category4



We used the Corel-2000 dataset with 2000 images, 20 classes and 100 images per class. Details about segmentation and feature extraction were mentioned in Section 6.2. Two experiments were carried out: one using the first 10 categories in the dataset (Corel-1000), and a second one using the complete dataset with all 20 categories (Corel-2000). Figure 12 shows images randomly sampled from the 20 categories. Table 8 presents the classification accuracy rates, including the results of DD-SVM, MILES and MILIS, as reported by the original papers, and the results of MI-SVM and mi-SVM, as reported in [2]. From Table 8, we can see that MILTree-Med and MILTree-SI outperform competing methods due to the efficient bag selection strategy used for training and the efficient instance prototype selection performed inside each bag.