Fusion of Rough Set Theory, Genetic Algorithm-Backpropagation Neural Networks and Shapley Additive Explanations for the Design of Bamboo Furniture

Abstract

In today's competitive market, meeting consumers' satisfaction and emotional needs is crucial for business success. However, the cognitive gap between designers and consumers often hinders market recognition for bamboo furniture. Therefore, a research framework based on Kansei Engineering (KE) is proposed in this study. First, the emotional needs and related samples were collected, and the sample form was deconstructed systematically. Then, the attribute reduction algorithm in rough set theory was used to extract the key emotional needs that have significant impact on consumer satisfaction. Finally, an intelligent mapping model between product components and emotional needs was constructed using Genetic Algorithm-Backpropagation Neural Networks (GA-BPNN), which predicts the optimal product design parameters that meet users' emotional needs. Additionally, we conducted an interpretative analysis of the prediction model using the Shapley Additive Explanations (SHAP) method. The evaluation results were significantly higher than the average, validating the advanced and effective nature of the method proposed in this study. Compared with previous KE studies, the GA-BPNN model proposed in this study has better prediction efficiency and higher precision, which can more effectively solve the cognitive differences between designers and consumers. Thus, the development efficiency and decision-making accuracy of enterprises’ product design has been improved.