###### Abstract  
The conversion from functional requirements (FRs) to design parameters is the foundation of product customization. However, original customer needs usually result in incomplete FRs, limited by customers' incomprehension on the design requirements of these products. As the incomplete FRs may undermine the design activities afterwards, managers need to develop an effective approach to predict the missing values of the FR. This study proposes an integrative approach to obtain the complete FR. The \(k\) nearest neighbor (KNN) algorithm is employed to predict the missing continuous variables in FR, using the improved distance formula for two incomplete FRs. Support vector machine (SVM) classifiers are adopted to classify the missing categorical variables in FR, combined with directed acyclic graph for multi-class classification. KNN and SVM are then integrated into a multi-layer framework to predict the missing values of FR, where categorical and continuous variables both exist. A case study on the elevator customization is conducted to verify that KNN-SVM is feasible in accurate prediction of elevator FR values. Furthermore, KNN-SVM outperforms other five single and five composite methods, with average reduction in root mean squared error (RMSE) of 39% and 21% against KNN and KNN-Tree, respectively. product customization; functional requirements; missing values prediction; categorical and continuous variables; KNN-SVM +
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affect the conversion from FR to DP. Subjective CN is easy to propose, such as appearance requirements in customized clothing [9]. Other quality requirements, which impose constraints on the design or implementation (such as performance requirements, security, or reliability) [10; 11], are too professional for customers to put forward. Some customers may leave some questions about requirement questionnaires consciously or unconsciously. In addition, developing product customization systems is popular in many manufacture sectors. However, the missing values of requirements or other aspects, caused by information gap, measurement error, and equipment failure, are neglected. On the other hand, each order is personalized in customization, where missing values prediction for each FR needs to be personalized. Missing values in FR is a potential problem to be addressed in requirements modeling of product customized design, which is the key motivation of this paper. In this context, a number of researches have been conducted to predict the missing personalized requirements, as detailed in Section 2.1. From a review of existing literature, this research is helpful to collect more correct CN, which is also helpful to transform CN into the more complete FR afterwards. However, this research is focused on predicting CN instead of FR [12; 13; 14; 15]. Many implicit CNs are still subjective requirements rather than quality requirements [16; 17; 18], which results in FR still be incomplete in parts of performance requirements, structure requirements, or other design requirements. Starting with incomplete FRs directly, this study aims to introduce an approach to predict the missing values of FR. The principle objectives of this study are:  
* Explore the interconnections among different orders from customers to predict the missing values of FR. * Determine the optimal predicted values with a feasible framework and algorithms for missing values prediction of FR. This study proposes an integrative approach to predict the missing values of FR in product customization. By analyzing the attributes and values of FR in industrial customized products, FR can be divided into continuous and categorical variables. Missing values in different attributes are predicted by different methods. Thus, a multi-layer  
Figure 1: Design activities in product customization and workflow of requirements modeling.