## 2 Literature Review
### Imputation Approaches for Missing Values  
Traditional missing values prediction methods in FR rely on managers' experience, investigation, and communications with customers, which are popular in mass production. In smart customization, intelligent methods should be proposed to solve this problem for a lot of personalized orders. Missing values imputation is a hotspot issue in data mining and machine learning nowadays. Requirements prediction in product customized design can learn from this research. We conclude the main methods of missing values imputation in Table 1 for reference.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Model/Methodology** & **Continuous Variables** & **Categorical Variables** & **Application** \\ \hline \multirow{4}{*}{Rule-based} & Imputation with master & & & \\  & data and a class of & \(\surd\) & & Two real-word datasets \\  & editing rules [20] & & & \\ \cline{2-5}  & Five strategies used for & & & \\  & different attributes [21] & \(\surd\) & \(\surd\) & Preterm birth datasets \\ \hline \multirow{4}{*}{Statistics-based} & Estimation of mean & & & \\  & values and covariance & \(\surd\) & & Surface temperature \\  & matrices [22] & & & data \\ \cline{2-5}  & Expectation & & & \\  & maximization with & \(\surd\) & \(\surd\) & Amelia II (software) \\  & bootstrapping [23] & & & \\ \hline \multirow{4}{*}{Statistics-based} & Gray KNN [24] & \(\surd\) & \(\surd\) & Six real-world datasets \\  & Sequential KNN [25] & \(\surd\) & & DNA microarray \\  & & & & analysis \\ \cline{2-5}  & Generalized regression & & & 30 synthetic datasets + \\  & neural network & & \(\surd\) & 67 public datasets + one \\  & (GRNN) [26] & & & new real-world dataset \\ \cline{2-5}  & Bayesian principal & & & & Gene expression profile \\  & component analysis & \(\surd\) & & data \\  & (BPCA) [27] & & & \\ \cline{2-5}  & Bicluster-based BPCA & \(\surd\) & & DNA microarray \\  & [28] & & & analysis \\ \cline{2-5}  & & & & Four datasets for \\ Model-based & & & & continuous, three \\  & Random forest [29] & \(\surd\) & \(\surd\) & datasets for categorical, \\  & & & & and three datasets for \\  & & & & mixed variables \\ \cline{2-5}  & Singular value & & & DNA microarray \\  & decomposition (SVD) & \(\surd\) & & & analysis \\  & with KNN [30] & & & & \\ \cline{2-5}  & Kernel density & & & & \\  & clustering combined & & & & Eight real-world \\  & with decision tree [31] & & & & datasets \\ \cline{2-5}  & Formal optimization & & & & \\  & framework with KNN, & \(\surd\) & & \(\surd\) & 84 real-word datasets \\  & SVM, and decision trees [32] & & & & \\ \hline \multirow{4}{*}{Human–computer} & Crowdsourcing & & & & Two real-world \\  & optimized by & \(\surd\) & & & datasets \\ \cline{1-1}  & knowledge base [33] & & & & Two real-world \\ \cline{1-1}  & Crowdsourcing with & \(\surd\) & & & \\ \cline{1-1}  & Bayesian network [34] & \(\surd\) &  
Data in missing values imputation are divided into continuous and categorical variables, which are adoptable to predict missing values of FR. However, there are a few methods considering continuous and categorical variables synchronously. To improve the accuracy of predicted values, it is necessary to propose and integrate different imputation methods for different characteristics of requirements. Model-based methods for missing values imputation outperform other methods, which seem appropriate to the problem at hand. In addition, the applications of these methods focus mostly on the public datasets. To the best of authors' knowledge, there are few research studies on the missing values prediction in FR. Existing methods need to be improved for practical applications in product customized design.