Emotional Cellular-Based Multi- Class Fuzzy Support Vector Machines on Product’s KANSEI Extraction

It is an important methodology to extract product’s overall KANSEI images by evaluating Critical Form Features (CFF). In this paper, Multi-class Fuzzy Support Vector Machines (MF-SVM) employing Emotional Cellular (EC) model was presented to extract KANSEI images of product’s CFF. EC is a very special kind of semantics cell, which is defined on two-dimensional (Valence-Arousal) emotional space. The shell of EC covers the areas of the boundary of each emotional word that reflects its uncertainty, in common, a density function was employed to reflect this uncertainty. Firstly, product from features was mapped into an N dimensional vector. Secondly, the norm of vector space and the fuzzy membership of each element are calculated by using probability density function of EC including Single Gaussian Model (SGM) and Gaussian Mixture Model (GMM). Finally, One-Versus-Rest (OVR) for multiclass SVMs was addressed to deal with multi-dimensional KANSEI images. For new products, system will specify all CFFs by using MF-SVMs. A case study of mobile phone design is given to demonstrate the effectiveness of the proposed methodology.