A layered KNN-SVM approach to predict missing values of functional requirements in product customization

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.