Probabilistic Intuitionistic Fuzzy Decision Making Algorithms

Abstract

In this paper, the probabilistic version of intuitionistic fuzzy decision making methods is introduced to show the duality present within probabilistic distance. Here, two types of methods, namely, probabilistic intuitionistic fuzzy TOPSIS (PI-TOPSIS) algorithm and probabilistic intuitionistic fuzzy face identification (PIFI) algorithm are proposed. The PI-TOPSIS is utilizing probabilistic distance as the separation measure; for its justification rankings and reduced information loss of the multi criteria decision making problems are compared. Our other proposed decision making method called PIFI algorithm handles Face Identification Problem. It is exemplified by better benchmark indexes of PIFI in comparison to support vector machine, naive bais classifier, fuzzy support vector machine algorithms, that, the probabilistic distance can be used as a similarity measure. The two well-known feature extraction techniques, called Local binary pattern (LBP) and Angular radial transformation (ART) are employed to extract the features in the face images. Further, it is concluded from the experimental findings, that, the proposed algorithms are adaptive in nature.

Keywords

• Supplier selection
• intuitionistic fuzzy set
• TOPSIS
• face identification