Product form feature selection for mobile phone design using LS-SVR and ARD

In the product design field, it is important to pin point critical product form features (PFFs) thatinfluence consumers' affective responses (CARs) of a product design. Manual inspection of critical form features based on expert opinions (such as those of product designers) has not proved to meet with the acceptance of consumers. In this paper, an approach based on least squares support vector regression (LS-SVR) and automatic relevance determination (ARD) is proposed to streamline the task of product form feature selection (PFFS) according to the CAR data. The representation of PFFs is determined by morphological analysis and pairwise adjectives are used to express CARs. In order to gather the CAR data, an experiment of semantic differential (SD) evaluation on collected product samples was conducted. The LS-SVR prediction model can be constructed using the PFFs as input data and the evaluated SD scores as output value. The optimal parameters of the LS-SVR model are tuned by using Bayesian inference. Finally, an ARD selection process is used to analyze the relative relevance of PFFs to obtain feature ranking. A case study of mobile phone design is also given to demonstrate the proposed method. In order to examine the effectiveness of the proposed method, the predictive performance is compared with a typical LS-SVR model using a grid search with leave-one out cross validation (LOOCV) and a multiple linear regression (MLR). The proposed model based on LS-SVR and ARD is outperformed to the other two model benefits from the soft feature selection mechanism. Furthermore, the resulting feature ranking is also compared with that of the MLR with backward model selection (BMS). The results of these two methods using the CAR data of three adjectives exhibit similar feature ranking. Since only one small data of mobile phone design are investigated, a more comprehensive investigation based on different kinds of products will be needed to verify the proposed method in our future study.