# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 4 Discussion  
The gcForest algorithm is a fusion of traditional machine learning algorithms and deep learning thought, but the standard gcForest algorithm may face over-fitting problems due to the small sample sizes and high dimensionality of genetic data. The MLW-gcForest algorithm is an improvement of the gcForest algorithm. Dynamically setting multi-level weights according to the classification performance of each random forest and different sliding windows can alleviate the problem of overfitting and improve the ability to classify small samples of gene data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cancer**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Result**} \\ \cline{3-5}  & & **AUC** & **ACC** & **Pre** \\ \hline \multirow{6}{*}{BRCA} & [PERSON] [22] & N/A & 0.87 & N/A \\  & [PERSON] [20] & N/A & N/A & 0.88 \\  & [PERSON] [41] & N/A & 0.91 & N/A \\  & [PERSON] [42] & 0.89 & N/A & N/A \\  & [PERSON] [43] & N/A & 0.89 & 0.90 \\  & MLW-gcForest & 0.98 & 0.91 & 0.92 \\ \hline \multirow{6}{*}{LUAD} & [PERSON] [22] & N/A & 0.91 & N/A \\  & [PERSON] [20] & N/A & N/A & 0.88 \\  & [PERSON] [41] & N/A & 0.86 & N/A \\  & [PERSON] [44] & 0.92 & N/A & N/A \\  & [PERSON] [19] & N/A & 0.85 & 0.86 \\  & MLW-gcForest & 0.92 & 0.87 & 0.86 \\ \hline \multirow{6}{*}{LHHC} & [PERSON] [20] & N/A & N/A & 0.82 \\  & [PERSON] [41] & N/A & 0.90 & N/A \\  & [PERSON] [45] & 0.77 & 0.83 & N/A \\  & [PERSON] [46] & N/A & 0.87 & N/A \\  & MLW-gcForest & 0.91 & 0.87 & 0.85 \\ \hline \multirow{6}{*}{GBM} & [PERSON] [20] & N/A & N/A & 0.78 \\  & [PERSON] [21] & 0.92 & 0.88 & N/A \\ \cline{1-1}  & [PERSON] [47] & 0.83 & 0.80 & N/A \\ \cline{1-1}  & MLW-gcForest & 0.87 & 0.89 & 0.86 \\ \hline \multirow{6}{*}{STAD} & [PERSON] [22] & N/A & 0.84 & N/A \\ \cline{1-1}  & [PERSON] [41] & N/A & 0.85 & N/A \\ \cline{1-1}  & MLW-gcForest & 0.88 & 0.87 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with the state of the art from the three indicators of AUC, ACC, and Pre on five data sets: (a) BRCA, (b) LUAD, (c) LHAC, (d) GBM, and (e) STAD. To demonstrate that the proposed MLW-gcForest can alleviate overfitting, we plotted the accuracy curves of MLW-gcForest and gcForest on the training and validation sets as the number of samples increased for five cancer subtypes. The results in Figure 7 show that under the five different cancer types, although the standard gcForest achieved good classification accuracy, the accuracy curves of standard gcForest on training and validation sets are further apart in position. It demonstrates that the accuracy of the standard gcForest on the validation set is much lower than that on the training set, which indicates that the standard gcForest still has some overfitting. The accuracy curve of MLW-gcForest on the training and validation sets is closer in position, compared to the standard gcForest. It demonstrates that the MLW-gcForest has a smaller difference in the accuracy of the validation set and the training set, which indicates that MLW-gcForest can effectively alleviate the over-fitting problem of the standard gcForest. Therefore, our dynamic multi-level weighting strategy can more effectively alleviate the over-fitting problem. Figure 7: _Cont._  
We compared the MLW-gcForest model with the standard gcForest model and several commonly used machine learning methods. We found that in the process of classifying cancer subtypes, the MLW-gcForest model and the gcForest model are superior to traditional classification methods. The most likely reason for the difference in performance is that deep forests can learn more meaningful advanced features through supervised learning. Among the subtypes of most cancers, the MLW-gcForest model is superior to the gcForest model, proving that our proposed multi-level weighting idea improves the classification ability of the standard gcForest algorithm for small sample high-dimension data and provides a good model for the classification of cancer subtypes.