A new parameter reduction algorithm for interval-valued fuzzy soft sets based on Pearson’s product moment coefficient

摘要

Interval-valued fuzzy soft set theory is a newly emerging mathematical tool for dealing with uncertain problems. Parameter reduction abandons redundant parameters meanwhile holds the powerful ability to support decision making Ma et al. (2014) expressed four diverse parameter reduction approaches which are appropriate for the different scenarios. Optimal choice considered parameter reduction approach is not effective to support the additive parameters. The other three methods provide the support for the newly additive parameters but possess very low rate of success. And the four methods are computationally complicated. In this paper, we propose Pearson’s product moment coefficient based parameter reduction algorithm for an interval-valued fuzzy soft sets. By comparison with four algorithms, this approach not only is carried out before getting the scores, give attention to the newly additive parameters, and has much higher probability to find parameter reduction, but also is not more computationally complicated. Therefore, this algorithm is the most efficient to support extension and combination of multiple evaluation systems based on interval-valued fuzzy soft set in the down-to-earth applications environment. The superiority and effectiveness of the proposed approach is demonstrated by this means of a suitable practical application case of on-line reservation for accommodation and twenty synthetic generated datasets.