Measuring consistency of two datasets using fuzzy techniques and the concept of indiscernibility: Application to human perceptions on fabrics

This paper presents an approach for developing a new consistency degree of two datasets, obtained from two different measuring systems on the same collection of items. In this approach, the concept of indiscernibility, frequently used in rough set approaches, is used to discover the classification consistency-based inclusion of one dataset to another. Next, in order to take into account the influence of neighboring relations of different data, we modify the previous index by proposing a fuzzy classification consistency-based inclusion degree. Also, the ordinal correlation between these two datasets, measured using a non-parametric method called Kendall׳s coefficient, is introduced. Finally, in order to create a reasonable integration of the previous two indices, a general consistency measure is constituted by introducing the expert knowledge into a fuzzy inference system. The overall procedure is believed to be capable of detecting nonlinear patterns lying beneath data while being safe to use a comparatively small number of experimental samples. Moreover, this new method can prevent the “black box” phenomenon encountered in many modeling techniques and produce robust and interpretable results. In practice, the proposed method is particularly significant for validating one measuring or evaluation system with respect to a standard reference. In order to validate the effectiveness of the proposed consistency degree, we apply it to study the relationship between tactile properties of a collection of fabric samples and their visual representations. The obtained results confirm that most of the tactile information can be perceived correctly by assessors through either video or image displays, while a better performance is detected in video scenarios.