Customer Acceptability Evaluation and Prediction Based on Ensemble Classifiers

Abstract:

In the highly competitive market, to meet consumer’s need is a critical factor for product success. So, acceptability evaluation and prediction is important in product development. This study developed an intelligent model to evaluate and predict consumer acceptability. The model used IG as ranking method to rank the features of importance firstly. In addition, it employed the Bayesian Network (BN) and Radial Basis Function (RBF) Networks and their ensembles to build a prediction model. To demonstrate applicability of the proposed model, we adopted a real case, mp3 evaluation, to show that the consumer acceptability problem can be easily evaluated and predicted using the proposed model. The results show that ensemble classifiers are more accurate than a single classifier. This ensemble model not only helps manufacturer in evaluating the importance of product features but also predicting consumer acceptability.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 576-581
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.576
• Online since: July 2012
• Authors: Feng Qian, Lin Wen Xu
• Keywords: Consumer Acceptability, Ensemble Classifiers, Evaluation, Feature Ranking, Prediction

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