# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Ablation Experiments  
To validate the accuracy and reliability of our proposed model, we conducted an ablation study by using two simplified models: our KRR regression version and our single direct MMPSO-SVR version. Figure 9A-H present the performance results on the few-shot human body dimension dataset. The comparative analysis involved assessing the predictive accuracy of the GRFN model in terms of the KRR regression and the single direct MMPSO-SVR models. Compared with the \(e_{RMSE}\) values of the single direct MMPSO-SVR model, those of the combination hybrid model increased by 97.12% maximally in crott width, and by 91.73% maximally in abdomen circumference. The \(e_{RMSE}\) and \(e_{MAE}\) values indicate that the hybrid model demonstrated superior predictive performance compared with every other model. The KRR regression model performed poorly in predicting \(R^{2}\) values across all measurement items, but the single unmodified MMPSO-SVR achieved \(R^{2}\) values above 0.9 for KH, AC, and AH predictions. However, it struggled with CW and TCL, which are particularly challenging to measure accurately, resulting in low \(R^{2}\) values. In contrast, the regression values of our hybrid model were above 0.999 for all prediction items, indicating that our method predicts body sizes relevant to garment pattern making more accurately and efficiently, effectively avoiding the shortcomings of single body dimension prediction methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Item** & \(e_{RMSE}\)\(\downarrow\) & \(e_{MAE}\)\(\downarrow\) & \(R^{2}\)\(\uparrow\) & **Item** & \(e_{RMSE}\)\(\downarrow\) & \(e_{MAE}\)\(\downarrow\) & \(R^{2}\)\(\uparrow\) \\ \hline MMPSO-SVR & & **1.5385** & **1.1225** & **0.8686** & & **1.7991** & **1.2943** & **0.7794** \\ HCLDMSPSO-SVR & & 1.5741 & 1.1335 & 0.8607 & & 2.0060 & 1.5564 & 0.7243 \\ HCLPSO-SVR & CH & 1.5709 & 1.1326 & 0.8609 & TCL & 2.0124 & 1.5222 & 0.7301 \\ PSO-LSSVM & & 1.5915 & 1.1492 & 0.8568 & & 2.0593 & 1.5325 & 0.7237 \\ BPNN & & 2.1858 & 1.4342 & 0.7219 & & 3.1221 & 2.2151 & 0.5894 \\ \hline MMPSO-SVR & & **0.5804** & 0.4429 & **0.9774** & & **1.3579** & **1.0440** & **0.9721** \\ HCLDMSPSO-SVR & & 0.6145 & 0.4360 & 0.9768 & & 1.3936 & 1.0637 & 0.9708 \\ HCLPSO-SVR & KH & 0.6105 & **0.4283** & 0.9765 & AC & 1.3933 & 1.0621 & 0.9709 \\ PSO-LSSVM & & 0.6109 & 0.4287 & 0.9765 & & 1.4113 & 1.0769 & 0.9698 \\ BPNN & & 0.6475 & 0.5337 & 0.9404 & & 1.6073 & 1.2638 & 0.9661 \\ \hline MMPSO-SVR & & **1.5479** & **1.3291** & **0.8602** & & **1.0930** & **0.8490** & **0.8212** \\ HCLDMSPSO -SVR & & 1.5734 & 1.3450 & 0.8542 & & 1.0932 & 0.8491 & 0.8210 \\ HCLPSO-SVR & TC & 1.5666 & 1.3373 & 0.8547 & KC & 1.0941 & 0.8635 & 0.8171 \\ PSO-LSSVM & & 1.5732 & 1.3475 & 0.8539 & & 1.0942 & 0.8512 & 0.8203 \\ BPNN & & 1.8710 & 1.4267 & 0.7507 & & 1.5102 & 1.1552 & 0.6559 \\ \hline MMPSO-SVR & & **0.5604** & **0.3884** & **0.7428** & & **0.4899** & **0.3629** & **0.9923** \\ HCLDMSPSO -SVR & & 0.5676 & 0.3972 & 0.7332 & & 0.5016 & 0.4321 & 0.9883 \\ HCLPSO-SVR & CW & 0.5655 & 0.3951 & 0.7356 & AH & 0.5119 & 0.4232 & 0.9882 \\ PSO-LSSVM & & 0.5671 & 0.4020 & 0.7309 & & 0.5369 & 0.5603 & 0.9799 \\ BPNN & & 0.5755 & 0.4521 & 0.7241 & & 0.9344 & 0.6180 & 0.9709 \\ \hline \hline \end{tabular} Notes: \(e_{RMSE}\) and \(e_{MAE}\) in centimeters. Symbol \(\downarrow\) represents that the smaller value, the better. Symbol \(\uparrow\) represent that the larger value, the better. Values in bold font indicate the optimal results.  
\end{table}
Table 8: Comparison of estimation results of single optimization SVR models (\(e_{RMSE}\) and \(e_{MAE}\) in cm).  
Figure 9: Performance comparison of regression model on small-sample body dimensions datasets: (**A**) crotch height; (**B**) total crotch length; (**C**) knee height; (**D**) abdomen circumference; (**E**) thigh circumference; (**F**) knee circumference; (**G**) crotch width; and (**H**) abdomen height.