# Regularized Instance Embedding for Deep Multi-Instance Learning
## 4 Experiments
### Results  
We conducted extensive experiments on various datasets for different applications, such as drug-activity prediction on the MUSK dataset, image classification on the Animal dataset, text classification on the 20 Newsgroups and Web Recommendation datasets, and cancer prediction on the Messidor and Ucsb_breast datasets. The results are listed and compared with state-of-the-art methods in Tables 3-7, respectively. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Dimension**} & \multicolumn{3}{c}{**Number of Bags**} & \multicolumn{2}{c}{**Number of Instances**} \\ \cline{3-6}  & & **Positive** & **Negative** & **Total** & **Average** & **Total** \\ \hline MUSK1 & 166 & 47 & 45 & 92 & 5 & 476 \\ MUSK2 & 166 & 39 & 63 & 102 & 65 & 6598 \\ Elephant & 230 & 100 & 100 & 200 & 7 & 1391 \\ Fox & 230 & 100 & 100 & 200 & 7 & 1320 \\ Tiger & 230 & 100 & 100 & 200 & 6 & 1220 \\ alt.atheism & 200 & 50 & 50 & 100 & 54 & 5443 \\ comp.graphics & 200 & 49 & 51 & 100 & 31 & 3094 \\ web1-train & 5863 & 17 & 58 & 75 & 29 & 2212 \\ web1-test & 5863 & 4 & 34 & 38 & 29 & 2212 \\ messidor & 687 & 654 & 546 & 1200 & 10 & 12,352 \\ UCSB\_breast & 708 & 26 & 32 & 58 & 35 & 2002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed dataset properties, including name of MIL dataset, instance dimension (\(N\) in Sections 3.3 and 4.1), number of positive and negative bags, total number of bags, average number of instances for each bag, and total number of instances. The Newsgroups dataset contains 20 sub-datasets, and only two sub-datasets are presented for simplification (alt.atheism and comp.graphics). The Web Recommendation dataset contains nine sub-datasets, and only one sub-dataset is presented for simplification (web1-train and web1-test for training and testing, respectively). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **MUSK1** & **MUSK2** \\ \hline mi-SVM [15] & 0.874 & 0.836 \\ MI-SVM [15] & 0.779 & 0.843 \\ MI-Kernel [37] & 0.880 & 0.893 \\ EM-DD [38] & \(0.849\pm 0.098\) & \(0.869\pm 0.108\) \\ mi-Graph [8] & \(0.889\pm 0.073\) & \(0.903\pm 0.086\) \\ miVLAD [51] & \(0.871\pm 0.097\) & \(0.872\pm 0.095\) \\ miFV [51] & \(0.909\pm 0.089\) & \(0.884\pm 0.094\) \\ MinD & \(0.893\pm 0.019\) & \(0.888\pm 0.034\) \\ MI-Net [5] & \(0.893\pm 0.099\) & \(0.872\pm 0.096\) \\ mi-Net [5] & \(0.889\pm 0.098\) & \(0.868\pm 0.113\) \\ \hline RIE & \(0.962\pm 0.060\) & \(0.937\pm 0.091\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of different methods for bag classification on MUSK1 and MUSK2 (task: molecule-activity prediction). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Dataset** & **web1** & **web2** & **web3** & **web4** & **web5** & **web6** & **web7** & **web8** & **web9** & **Average** \\ \hline Citation-kNN [34] & 0.879 & 0.768 & 0.763 & 0.816 & 0.758 & 0.816 & 0.684 & 0.684 & 0.653 & 0.758 \\ Frecti-kNN [35] & 0.911 & 0.868 & 0.858 & 0.847 & 0.853 & 0.863 & 0.758 & 0.690 & 0.648 & 0.811 \\ G3P-MI [36] & 0.842 & 0.737 & 0.842 & 0.842 & 0.842 & 0.790 & 0.842 & 0.605 & 0.684 & 0.781 \\ MI-Net [5] & 0.916 & 0.866 & 0.824 & 0.916 & 0.724 & 0.818 & 0.758 & 0.703 & 0.666 & 0.799 \\ mi-Net [5] & 0.911 & 0.905 & 0.858 & 0.803 & 0.742 & 0.811 & 0.718 & 0.598 & 0.589 & 0.771 \\ \hline RIE & 0.916 & 0.937 & 0.909 & 0.934 & 0.708 & 0.829 & 0.868 & 0.747 & 0.768 & 0.846 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy of different methods for bag classification on Web Recommendation (task: text categorization).