An Evaluation of Website Upgrade Options: A Case Study Comparison of ANFIS and RIMER

Increases in usage of websites as communication tools has lead to reductions in costs of product and service delivery for companies. Incorrect design of websites leads to redesigning them to fit customer needs. Redesign costs are particularly relevant for resource constrained SMEs. Modeling highly subjective, uncertain and repetitive decisions requires methods that include the benefits of both expert knowledge as well as machine learning. This paper reports a comparison on the application of two such hybrid approaches: Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach (RIMER) and Artificial Neural Fuzzy Inference System (ANFIS). A numerical example and a case study are reported in order to analyze the potential of the two methods. The results show that both methods can be used to accurately represent and predict the complicated and nonlinear relationships between website characteristics and overall satisfaction. The RIMER approach is found to outperform ANFIS when considering explanatory power.