Constructing intelligent model for acceptability evaluation of a product

In the highly competitive marketplace, consumer acceptability has become an important factor in the product design process. However, manufacturers and designers often misunderstand what consumers really want. Thus, acceptability evaluation and prediction is important in product development. This study developed an intelligent model to solve consumer acceptability problem in an attempt to evaluate consumer acceptability with better performance. The model adopted three well-known feature ranking methods to rank features of importance. In addition, it employed the Bayesian Network (BN), Radial Basis Function (RBF) Networks, Support Vector Machine–Sequential Minimal Optimization (SVM–SMO) and their ensembles to build prediction models. In this study, we also focus on the use of non- parametric statistical test for the comparison algorithms performance in classification. To demonstrate applicability of the proposed model, we adopted a real case, car evaluation, to show that the consumer acceptability problem can be easily evaluated and predicted using the proposed model. The results show that the model can improve the performance of consumer acceptability problems and can be easily extended to other industries.