# Ensemble of Networks for Multilabel Classification
## 7 Conclusions  
The system proposed in this work for multilabel classification is composed of ensembles of gated recurrent units (GRU), temporal convolutional neural networks (TCN) and long short-term memory networks (LSTM) trained with several variants of Adam optimization. This approach combines Incorporating Multiple Clustering Centers (IMCC) to produce an even better multilabel classification system. Many ensembles are tested across a set of twelve multilabel benchmarks representing many different applications. Experimental results show that the best ensemble constructed using our novel approach obtains state-of-the-art performance. Future studies will focus on combining our proposed method with other topologies for extracting features. Moreover, more sparse datasets will be used to evaluate the per \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**AVage Precision** & **Cal500** & **Image** & **Scene** & **Yeast** & **Arts** & **ATC** & **ATC\_f** & **Liu** & **mAn** & **Bibtex** & **Enron** & **Health** \\ \hline Ens & 0.503 & 0.849 & **0.912** & 0.780 & 0.632 & 0.878 & **0.926** & 0.527 & 0.981 & 0.626 & 0.717 & 0.788 \\ EnsSparse & 0.500 & **0.851** & 0.898 & 0.786 & **0.641** & **0.883** & 0.920 & **0.538** & **0.983** & **0.635** & 0.727 & **0.800** \\ FastAi [32] & 0.425 & 0.824 & 0.899 & 0.718 & 0.588 & 0.860 & 0.908 & 0.414 & 0.976 & — & — & — & — \\ IMCC & 0.502 & 0.836 & 0.904 & 0.773 & 0.619 & 0.866 & 0.922 & 0.523 & 0.978 & 0.623 & 0.714 & 0.781 \\ hML & 0.453 & 0.810 & 0.885 & **0.792** & 0.538 & 0.831 & 0.854 & 0.433 & 0.965 & — & — & — & — \\ ECC [5] & 0.491 & 0.797 & 0.857 & 0.756 & 0.617 & — & — & — & — & — & 0.617 & 0.657 & 0.719 \\ MAHR [5] & 0.441 & 0.801 & 0.861 & 0.745 & 0.524 & — & — & — & — & — & 0.524 & 0.641 & 0.715 \\ LLSF [5] & 0.501 & 0.789 & 0.847 & 0.617 & 0.627 & — & — & — & — & — & 0.627 & 0.703 & 0.780 \\ JFSC [5] & 0.501 & 0.789 & 0.836 & 0.762 & 0.597 & — & — & — & — & — & 0.597 & 0.643 & 0.751 \\ LIFT [5] & 0.496 & 0.789 & 0.859 & 0.766 & 0.627 & — & — & — & — & — & 0.627 & 0.684 & 0.708 \\ [15] & — & — & — & — & — & — & — & — & — & 0.513 & — & — & — & — & — \\ [72] & — & — & — & — & — & — & — & — & — & 0.261 & — & — & — & — & — & — \\ hMuLab [18] & — & — & — & — & 0.778 & — & — & — & — & — & — & — & — & — & — \\ MIKnn [18] & — & — & — & 0.766 & — & — & — & — & — & — & — & — & — & — \\ RaKeL [18] & — & — & — & — & 0.715 & — & — & — & — & — & — & — & — & — \\ ClassifierChain [18] & — & — & — & 0.624 & — & — & — & — & — & — & — & — & — & — \\ IBLR [18] & — & — & — & — & 0.768 & — & — & — & — & — & — & — & — & — \\ MLDLP [73] & 0.512 & 0.842 & 0.891 & 0.770 & — & — & — & — & — & — & — & — & 0.742 & — \\ RF\_PCT [73] & 0.512 & 0.829 & 0.873 & 0.758 & — & — & — & — & — & — & — & 0.729 & — \\ DBPNN [73] & 0.495 & 0.672 & 0.563 & 0.738 & — & — & — & — & — & — & 0.679 & — \\ MLFE [73] & 0.488 & 0.817 & 0.882 & 0.759 & — & — & — & — & — & — & 0.656 & — \\ ECC [73] & 0.482 & 0.739 & 0.853 & 0.752 & — & — & — & — & — & — & 0.646 & — \\ RAKEL [73] & 0.353 & 0.788 & 0.843 & 0.720 & — & — & — & — & — & — & 0.596 & — \\
[14] & — & — & — & 0.758 & — & — & — & — & — & — & — & — & — & — \\
[74] & 0.484 & — & — & 0.740 & — & — & — & — & — & — & — & — & — \\
[75] & — & — & — & — & 0.775 & 0.636 & — & — & — & — & — & — & — & — \\ Wrap [76] & **0.520** & — & 0.832 & 0.761 & — & — & — & — & — & & 0.578 & 0.710 & — \\ Wrapk [76] & 0.518 & — & 0.892 & 0.781 & — & — & — & — & — & & 0.571 & 0.720 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparisons with results reported in a recent survey [71] using average precision as the performance indicator. Bold highlights superior performance. \begin{table}
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**AVage** & **CMR** & **MLAN** &  
formance of the proposed ensemble for further validation of the conclusions drawn in this work. Another area of research will be to investigate deep ensembling techniques for multi-label classification that utilizes edge intelligence.