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

This section presents the experiments carried out to compare the proposed methods with MIL methods available for each dataset. We evaluate the average precision, average recall and average accuracy of both MILTree-Med and MILTree-SI using the MILTree layout for five MIL benchmark datasets, Corel-1000 and Corel-2000 image classification datasets, as well as the Biocreative text classification dataset. Furthermore, we perform experiments on a large-scale dataset and in a multiclass problem, both not addressed in previous works. The proposed methodology called MILSIPTree is used to carry out the complete multiple-instance classification process. Note that MILSIPTree is supported by both MILTree-Med and MILTree-SI instance prototype selection methods and the MILTree layout as well. The source code of the proposed methods and the multi-instance datasets are publicly available to allow reproducibility. The LIBSVM package was applied to train all SVMs using settings that are comparable with the results obtained by the competing methods. For the Musk1 and Musk2 datasets, we have employed the classifier _nu-SVC (Nu-Support Vector Classification)_, with a _Nu_ value equal to 0.6. For the images datasets (Elephant, Fox, Tiger, Corel-1000 and Corel-2000) and text dataset (Biocreative), we used the classifier _C-SVC (C-Support Vector Classification)_, with a _Cost_ value equal to 1. No kernel was used because we intended to show how the proposed methods help obtaining a feature space with linearly separable classes, which require less effort on designing the classifier. ### Benchmark Datasets

Five standard MIL benchmarks were used: The Musk1 and Musk2 datasets [3], as well as Elephant, Fox and Tiger image datasets [11]. Those have been widely used in multiple-instance learning studies. Musk1 and Musk2 are real-world benchmark datasets available at the UCI machine learning repository [36]. The Musk data were generated in the research of drug activity prediction, in which a drug molecule is represented by a bag, and the different structural conformations of this molecule are considered as instances. Musk1 contains 47 positive bags and 45 negative bags, and the number of instances contained in each bag ranges from 2 to 40. Musk2 contains 39 positive bags and 63 negative bags, and the number of instances contained in each bag ranges from 1 to 1044. Each instance is represented by 166 continuous attributes. Table 1 shows the detailed information on the Musk datasets. The image datasets named Elephant, Fox and Tiger were built with the goal of discriminating images containing elephants, foxes and tigers from those that do not, respectively. In this case, bags are considered images, and instances are considered regions of interest within the images. More details about these datasets are given in \[s_{i,u}=\frac{D_{i,j}}{2}+\frac{r_{i}+r_{j}}{2(m-2)}, \tag{6}\]0

We split each dataset into 30% training and 70% testing data. Our methods allow the training set to be small because they provide smart ways to select it so that it is representative enough. The model update was similar to the case studies 1 and 2: the initial model is updated by either changing the prototypes using MILTree-Med or including new prototypes using MILTree-SI. Table 3 compares the results achieved by MILTree-SI and MILTree-Med in detail. Additionally, in Table 4, we compare MILTree-SI and MILTree-Med with the accuracy reported by nine MIL algorithms from the literature: Four baseline methods such as EM-DD [23], DD-SVM [37], mi-SVM[11] and MI-SVM[11], and five methods that use instance selection such as MILES [15], MILIS [2], MILD-B [38] as well as the most recent state-of-the-art results from MILSIS [16] and MILDE [4]. The best accuracies are shown in bold. The actual number of bags used to update the initial model was between three and eight. These bags were selected through the visual analysis of the data and by choosing alternative prototypes that were automatically detected by using either SI or Med approaches.