## 5 Case Study
### Experimental Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMSE\({}_{\textbf{o}}\)**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**Time (s)**} & \multicolumn{3}{c}{**Ang**} \\ \cline{2-13}  & **10\%** & **30\%** & **50\%** & **10\%** & **30\%** & **50\%** & **10\%** & **30\%** & **50\%** & **10\%** & **30\%** & **50\%** \\ \hline KNN-GRNN & 0.53 & 0.52 & 0.56 & 0.58 & 0.57 & 0.59 & 33 & 111 & 222 & 0.01 & 0.02 & 0.05 \\ GRNN & 0.59 & 0.56 & 0.56 & 0.61 & 0.59 & 0.59 & 73 & 254 & 479 & 0.03 & 0.10 & 0.13 \\ \hline KNN-SVD & 0.48 & 0.51 & 0.52 & 0.57 & 0.57 & 0.57 & 0.27 & 0.29 & 0.30 & 0.01 & 0.03 & 0.04 \\ SVD & 0.55 & 0.55 & – & 0.64 & 0.60 & – & \textless{}0.01 & \textless{}0.01 & – & 0.02 & 0.04 & – \\ \hline KNN-BBPCA & 0.49 & 0.49 & 0.49 & 0.53 & 0.52 & 0.54 & 74 & 91 & 88 & 0.01 & 0.03 & 0.04 \\ BBPCA & 0.51 & 0.51 & 0.52 & 0.52 & 0.53 & 0.54 & 85 & 89 & 62 & 0.01 & 0.03 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of single and composite methods. Figure 5: Performance of different single methods to predict the missing values in functional requirements (FRs) (**a**) RMSE of categorical variables; (**b**) root mean squared error (RMSE) of continuous variables; (**c**) computation time. (Figure 6c), but KNN-SVM results in average reduction in RMSE of 46%, 39%, 30%, and 21% against KNN-SVD, KNN, KNN-Bay, and KNN-Tree, respectively (Figure 6b). SVM usually needs longer computation time for classification, which is its own short slab. Although the computation time is not the shortest, SVM is feasible in the real-world application with around 1.5 s for computation. With the missing rate increasing, the accuracy of prediction decreases, but the performance of similarity in KNN-SVM outperforms the others even in high missing rate. In the proposed framework, KNN-SVM is the most feasible method for practical application. In addition, we randomly choose 10 and 50 FRs in \(\mathbf{X}\) for test in cold- and warm-start scenarios. Cold-start scenario refers to the initial scenario for FR prediction, where there are no complete FRs for reference. Usually, experts are required to predict the missing values of FR through their own experience, which is often in the early stage of the new customized product development. Warm-start scenario means there is a number of completed FRs in the resource library. It is the common scenario in product customization. Figure 6: Performance of different composite methods to predict the missing values in FR (**a**) RMSE of categorical variables; (**b**) RMSE; (**c**) computation time; (**d**) Ang; (**e**) Len; (**f**) FSN. As shown in Table 3, all methods can only provide the predicted suggestions to managers with low accuracy in the cold-start scenario, especially with few incomplete FRs. For only 10 FRs in the cold-start scenario, the KNN method cannot find the accurate nearest neighbors for prediction, and the metric FSN is not applicable for all methods. Comparing cold-start scenarios 1 and 2, although all FRs are incomplete, more FRs can improve the performance. When the number of incomplete FRs increases, the proposed approach is useful and beneficial, and the predicted results are valuable to managers. Comparing the different start scenarios, KNN-SVM shows better performance in the warm-start scenario. The values of Ang are minimum, which means the predicted values are very similar to the ground truth. If there is a small quantity of incomplete FRs and a mass of complete FRs for reference, the predicted results will be more accurate. It is obvious that KNN-SVM always outperforms other methods and the computation time is acceptable in Table 3. We then make a concrete analysis of the predicted values of a new FR in Section 5.3.