A new fuzzy hybrid technique for ranking real world Web services

Abstract

We propose in this article a new fuzzy hybrid ranking technique, which is based on a linear combination of two new ranking techniques we devised: an objective Fuzzy Distance Correlation Ranking Technique (FDCRT) and a subjective Fuzzy Interval-based Ranking Technique (FSIRT). The objective technique leverages the distance correlation metric to derive weights of quality attributes directly from the available data. The subjective technique computes weights from opinions of domain experts, which are specified via two ingredients: intervals representing acceptable ranges of values for quality attributes and importance values of a quality attribute with respect to the other attributes. We show that the linear combination of these two techniques allows to overcome the shortcomings of objective and subjective techniques. Our experiments are performed on a dataset of real world Web services. The empirical results show that a tuning of the proposed linear combination gives better ranking results than Entropy and Fuzzy AHP separately and even than a linear combination of these two well-known techniques.