Emotional Cellular-Based Multi- Class Fuzzy Support Vector Machines on Product's KANSEI Extraction
作者： Fuqian Shi , Jiang Xu
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摘要: 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 of CFF. EC a very special kind semantics cell, which defined on two-dimensional (Valence-Arousal) emotional space. The shell covers the areas boundary each word that reflects its uncertainty, in common, density function employed reflect uncertainty. Firstly, product from features mapped into N - dimensional vector. Secondly, norm vector space and fuzzy membership element are calculated using probability including Single Gaussian Model (SGM) Mixture (GMM). Finally, One-Versus-Rest (OVR) for multi- class SVMs addressed deal with multi-dimensional images. For new products, system will specify all CFFs MF-SVMs. A case study mobile phone design given demonstrate effectiveness proposed methodology.
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