# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
The results show that the proposed MILTree-SI and MILTree-Med methods are very competitive, especially MILTree-Med, achieving an overall average performance of 82.8%. ### Image Classification

We used the Corel-1000 image dataset to evaluate the performance of MILTree-Med and MILTree-SI using the MILTree layout for analyzing multi-instance data. It contains 10 subcategories representing distinct topics of interest. Each subcategory contains 100 images. Since the original Corel dataset was not designed for multi-instance learning, we adopt the same approach of [15; 37] to segment each image into several regions. The second column of Tables 5 and 6 presents the average number of instances per bag for each category. We then choose one category as the positive class and select 100 images uniformly from the remaining categories to create the negative class as is performed in [11]. The same process is followed for each category, totaling 10 subsets of categories. In this section, each subdataset is split into 20% training and 80% testing data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Bags**} & \multicolumn{2}{c}{**Instances**} & \\ \cline{2-5}  & **Total** & **Pos./Neg.** & **Total** & **Min/Max** & **Dim** \\ \hline Musk1 & 92 & 47/45 & 476 & 2/40 & 166 \\ Musk2 & 102 & 39/63 & 6598 & 1/1044 & 166 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Musk datasets and the average number of instances per bag(Inst/Bag) for each dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**MILTree-Med**} & \multicolumn{4}{c}{**MILTree-SI**} \\ \cline{2-7}  & **Accur** & **Prec** & **Recall** & **F1** & **Accur** & **Prec** & **Recall** & **F1** \\ \hline Musk1 & **83.2** & 83.23 & 81.7 & 0.82 & **83.2** & 82.4 & 81.7 & 0.82 \\ Musk2 & **91.8** & 91.4 & 91.4 & 0.91 & 85.4 & 84.4 & 84.3 & 0.84 \\ Elephant & **83.1** & 81.7 & 81.6 & 0.82 & 81.4 & 79.4 & 79.4 & 0.79 \\ Fox & **72.7** & 68.3 & 68.3 & 0.68 & **72.7** & 68.3 & 68.3 & 0.68 \\ Tiger & **83.0** & 82.0 & 81.4 & 0.82 & 82.9 & 83.4 & 81.4 & 0.82 \\ \hline \hline \end{tabular} Bold values indicate the method that obtains the highest accuracy for each dataset.

\end{table} 
Table 3: Results of classification using MILTree-Med and MILTree-SI on the benchmark datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Musk1** & **Musk2** & **Elephant** & **Fox** & **Tiger** & **Avg.** \\ \hline MILTree-Med & 83.2 & **91.8** & 83.1 & **72.7** & **83.0** & **82.8** \\ MILTree-SI & 82.3 & 85.4 & 81.4 & 72.7 & 82.9 & 81.1 \\ EM-DD & 84.8 & 84.9 & 78.3 & 56.1 & 72.1 & 75.2 \\ MI-SVM & 77.9 & 84.3 & 73.1 & 58.8 & 66.6 & 72.1 \\ mi-SVM & 87.4 & 83.6 & 80 & 57.9 & 78.9 & 77.6 \\ DD-SVM & 85.8 & 91.3 & 83.5 & 56.6 & 77.2 & 79.0 \\ MILD-B & 88.3 & 86.8 & 82.9 & 55.0 & 75.8 & 77.8 \\ MILIS & 88.6 & 91.1 & - & - & - & - \\ MILES & 86.3 & 87.7 & 84.1 & 63.0 & 80.7 & 80.4 \\ MILSIS & **90.1** & 85.6 & 81.8 & 66.4 & 80.0 & 80.9 \\ MILDE & 87.1 & 91.0 & **85** & 66.5 & 83.0 & 82.5 \\ \hline \hline \end{tabular} Bold values indicate the method that obtains the best performance for each dataset.

\end{table} 
Table 4: Comparison between MILTree-SI / MILTree-Med and related methods from the literature on the benchmark datasets. The classification accuracy, precision and recall rates for both experiments are reported in Tables 5 and 6. In both tables, _Proto_ represents the number of instance prototypes that were updated in the bag or instance space layouts. _AddBags_ represents the number of bags that were included in the training data from the bag space layout. _AddProto_ represents the number of new instances \(B_{iy}\) included from the bags belonging to training data, which represents the MILTree-SI approach. Recall this was designed to identify additional prototypes to be included in the current model to reinforce the representation of misclassified bags.