# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 4 Performance Evaluation
### Effectiveness
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Azotobacter Vinelanduli**} & \multicolumn{4}{c}{**Evaluation Criterion**} \\ \cline{3-6}  & \(AP\uparrow\) & \(C\downarrow\) & \(OE\downarrow\) & \(RL\downarrow\) \\ \hline \multirow{4}{*}{MIMLSVM+} & \(Cost=10^{-3}\). \(\gamma=2^{3}\) & 0.5452 & 1.3171 & 0.6341 & 0.2679 \\  & \(Cost=10^{-2}\). \(\gamma=2^{2}\) & 0.5652 & 1.0732 & 0.6829 & 0.2312 \\  & \(Cost=10^{-1}\). \(\gamma=2^{3}\) & **0.6863** & 1.1707 & **0.5854** & **0.1927** \\ \cline{2-6}  & \(Cost=1\). \(\gamma=2^{4}\) & 0.5680 & 1.0488 & 0.6097 & 0.3301 \\  & \(Cost=10^{-1}\). \(\gamma=2^{4}\) & 0.6456 & **1.0244** & 0.6160 & 0.2435 \\  & \(Cost=10^{2}\). \(\gamma=2^{5}\) & 0.5308 & 1.9512 & 0.7317 & 0.2150 \\  & \(Cost=10^{3}\). \(\gamma=2^{4}\) & 0.5380 & 1.9756 & 0.6829 & 0.2191 \\ \hline \multirow{4}{*}{MIMLELM} & \(HN=100\) & 0.6453 & 1.4732 & 0.6414 & 0.2292 \\  & \(HN=200\) & 0.6622 & 1.3610 & 0.6658 & 0.2129 \\ \cline{1-1}  & \(HN=300\) & 0.6574 & 1.4585 & 0.6366 & 0.2318 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effectiveness comparison on the Azotobacter vinelanduli (Azoto.) dataset. As mentioned, we are the first to utilize ELM in the MIML problem. In this sense, it is more suitable to consider the proposed MIML-ELM as a framework addressing MIML by ELM. In other words, any better variation of ELM can be integrated into this framework to improve the effectiveness of the original one. For example, some recently-proposed methods, RELM [46], MCVELM [47], KELM [48], DropELM [49] and GEELM [50], can be integrated into this framework to improve the effectiveness of MIMLEM. In this subsection, we conducted a special set of experiments to check how the effectiveness of the proposed method could be further improved by utilizing other ELM learning processes instead of the original one. In particular, we replaced ELM exploited in our method by RELM [46], MCVELM [47], KELM [48], DropELM [49] and GEELM [50], respectively. The results of the effectiveness comparison on four different datasets are shown in Tables 6-9, respectively. As expected, the results indicates that the effectiveness of our method can be further improved by utilizing other ELM learning processes instead of the original one. As mentioned, we are the first to utilize ELM in the MIML problem. In this sense, it is more suitable to consider the proposed MIML-ELM as a framework addressing MIML by ELM. In other words, any better variation of ELM can be integrated into this framework to improve the effectiveness of the original one. For example, some recently-proposed methods, RELM [46], MCVELM [47], KELM [48], DropELM [49] and GEELM [50], can be integrated into this framework to improve the effectiveness of MIML-ELM. In this subsection, we conducted a special set of experiments to check how the effectiveness of the proposed method could be further improved by utilizing other ELM learning processes instead of the original one. In particular, we replaced ELM exploited in our method by RELM [46], MCVELM [47], KELM [48], DropELM [