# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 5 Conclusions  
Cancer is a highly heterogeneous disease, and different subtypes of cancer require different treatments. The subtype classification of cancer plays an important role in the diagnosis and treatment of cancer. In this paper, we propose a learning model called MLW-gcForest, which is an improved version of the standard gcForest algorithm, to improve the subtype classification ability of small sample and high dimensionality cancer genetic data. We fully consider the mutual synergy between different forests, assigning different weights to different random forests according to the classification ability of the forest.  
Furthermore, we propose a sorting optimization algorithm that assigns different weights to the feature vectors generated under different sliding windows, fully considering the complementarity of the feature vectors under different scanning windows. Specifically, the methylation data of five types of cancers, BRCA, LUAD, LIHC, GBM, and STAD, were used for classification. The experimental results show that the MLW-gcForest algorithm is superior to common machine learning algorithms in various evaluation metrics and also has better classification performance than the standard gcForest algorithm. The effectiveness of the proposed multi-level weighting scheme is shown, fully considering the diversity and complementarity of different random forests and different sliding windows; thus, more abundant and differentiated feature information is obtained, greatly improving the classification accuracy. Our study shows that methylation data are beneficial in the classification of cancer subtypes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Methylation**} & \multicolumn{3}{c}{**RNA**} & \multicolumn{3}{c}{**CNV**} \\ \cline{2-13}  & **ACC** & **Pre** & **Recall** & **F1** & **ACC** & **Pre** & **Recall** & **F1** & **ACC** & **Pre** & **Recall** & **F1** \\ \hline BRCA & 0.915 & 0.923 & 0.916 & 0.919 & 0.844 & 0.851 & 0.846 & 0.848 & 0.757 & 0.722 & 0.745 & 0.733 \\ LUAD & 0.866 & 0.863 & 0.852 & 0.857 & 0.807 & 0.826 & 0.824 & 0.825 & 0.739 & 0.746 & 0.726 & 0.736 \\ LIHC & 0.873 & 0.845 & 0.829 & 0.837 & 0.796 & 0.816 & 0.810 & 0.813 & 0.726 & 0.731 & 0.744 & 0.737 \\ GBM & 0.885 & 0.863 & 0.878 & 0.870 & 0.843 & 0.832 & 0.846 & 0.839 & 0.750 & 0.733 & 0.742 & 0.737 \\ STAD & 0.876 & 0.872 & 0.821 & 0.846 & 0.739 & 0.725 & 0.714 & 0.719 & 0.668 & 0.673 & 0.676 & 0.674 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance for different types of cancer and different types of data.  
Figure 8: Accuracy by different numbers of trees for five cancers.  
**Author Contributions:** Methodology, Y.D. and W.Y.; Software, J.W.; Validation, J.Z.; Writing--review and editing, Y.D., W.Y., and Y.Q.  
**Funding:** This research was funded in part by National Natural Science Foundation of China, grant number 61872261, in part by the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. 2018-VRLAB2018B07) and in part by Research Project Supported by Shanxi Applied Basic Research Project (201801D121139).  
**Conflicts of Interest:** The authors declare no conflict of interest.