A detailed analysis of the distance and similarity measures for intuitionistic fuzzy sets proposed in the past is presented in this paper. This study aims to highlight the main theoretical and computational properties of the measures under study, while the relationships between them are also investigated. Along with the literature review, a comparison of the analyzed distance and similarity measures from a pattern recognition point of view in three different classification cases is also presented. Initially, some artificial counter intuitive recognition cases are considered, while in a second phase real data from medical and well known pattern recognition benchmark problems are used to examine the discrimination abilities of the studied measures. Moreover, all the measures are applied in a face recognition problem for the first time and useful conclusions are drawn regarding the accuracy and confidence of the recognition results. Finally, the measures’ suitability and their drawbacks that make the development of more robust and efficient measures’ a still open issue are discussed.
G.A. Papakostas, A.G. Hatzimichailidis, V.G. Kaburlasos, “Distance and similarity measures between intuitionistic fuzzy sets: a comparative analysis from a pattern recognition point of view”, Pattern Recognition Letters, vol. 34, no. 14, pp. 1609-1622, 2013.