# A Visual Mining Approach to Improved Multiple-Instance Learning
## 4 Visual Multiple-Instance Learning
### Creating a Multiple-Instance Tree (MILTree)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **EMD** & **mi-SVM** & **MI-SVM** & **DD-SVM** & **SMILES** & **MILTree-SI** & **MILTree-Med** \\ \hline Cat0 & 68.7 & 71.1 & 69.6 & 70.9 & 72.4 & 68.1 & **76.0** \\ Cat1 & 56.7 & 58.7 & 56.4 & 58.5 & 62.7 & 75.8 & **79.1** \\ Cat2 & 65.1 & 67.9 & 66.9 & 68.6 & 69.6 & 76.5 & **79.0** \\ Cat3 & 85.1 & 88.6 & 84.9 & 85.2 & 90.1 & 72.9 & **91.3** \\ Cat4 & 96.2 & 94.8 & 95.3 & **96.9** & 96.6 & 83.7 & 78.4 \\ Cat5 & 74.2 & 80.4 & 74.4 & 78.2 & 80.5 & 80.9 & **81.9** \\ Cat6 & 77.9 & 82.5 & 82.7 & 77.9 & 83.3 & 89.1 & **89.1** \\ Cat7 & 91.4 & 93.4 & 92.1 & 94.4 & **94.7** & 80.3 & 84.2 \\ Cat8 & 70.9 & 72.5 & 67.2 & 71.8 & 73.8 & 75.2 & **81.2** \\ Cat9 & 80.2 & 84.6 & 83.4 & 84.7 & **84.9** & 75.8 & 81.3 \\ \hline \hline \end{tabular} Bold values indicate the method that obtains the best performance in each dataset.

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Table 7: Comparison between MILTree-SI/MILTree-Med and related methods from the literature on the Corel Dataset. Figure 12: Images randomly sampled from 20 categories of the COREL dataset and the corresponding segmentation results. Segmented regions are shown in their representative colors. ### Scalability Analysis on a MIL Text Classification

In MIL text classification, each document is represented as a bag and the document paragraphs as instances. The Biocreative dataset used in this experiment has 1623 documents (papers) extracted from biomedical journals, belonging to three text categories: Components, Processes and Functions, all referring to Gene Ontologies (GOs) [7]. It contains 34,569 instances, posing a challenge to conventional visualizations. Table 9 details this dataset. Each text document in the collection has a Protein identification, an associated article ID in PUBMED and a description text. We split the dataset in about 10% training and 90% testing data. After selecting the training set with MILTree and using the proposed prototype selection methods, all training bags were correctly classified, which means that all instances chosen as instance prototypes by MILTree-SI and MILTree-Med selection methods were representative. To compare our methods with other state-of-the-art methods, we employed the Weka machine learning package ([[http://www.cs.waikato.ac.nz/ml/weka](http://www.cs.waikato.ac.nz/ml/weka)]([http://www.cs.waikato.ac.nz/ml/weka](http://www.cs.waikato.ac.nz/ml/weka))) (accessed on 20 August 2021). There are no previous results for this dataset employing the aforementioned methods, such as MILES, SMILES and others, for the three categories of Biocreative dataset. Previous work only shows results for one category, such as [39], that presents result only for the \"Process\" category. For this reason, we compare our methods with multi-instance methods available in Weka, such as DD, EM-DD, MI-SVM, MIWrapper [40], TLDSimple [41] and MIBoost [42]. Table 10 shows the results, where both MILtree-Med and MILTree-SI methods supported by the MILTree layout obtained higher accuracy. The results can be explained by the visual discrimination of the categories \"Components\", \"Processes\" and \"Functions\" in the bag space projection of MILTree (see Figure 13a), which provides a clear guideline for users when identifying representative bags for the training set. This corroborates previous results that favor the strategy of selecting samples from both the internal and external parts of the MILTree. In Figure 13b, we show the training sample selected following the established guidelines. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **1000-Corel** & **2000-Corel** \\ \hline MILTree-Med & **93.1** & **93.9** \\ MILTree-SI & 90.3 & 93.9 \\ MI-SVM & 75.1 & 54.6 \\ mi-SVM & 76.4 & 53.7 \\ DD-SVM & 81.5 & 67.5 \\ MILIS & 83.8 & 70.1 \\ MILES & 82.3 & 68.7 \\ MILDE & - & 74.8 \\ \hline \hline \end{tabular} Bold values indicate the method that obtains the best performance in each dataset.

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