Uncertain rank correlation analysis based on normal interval value

Issue title: Special Section: Recent Advances in Machine Learning and Soft Computing

Guest editors: Srikanta Patnaik

Article type: Research Article

Authors: Wang, Bo^{; *} | Jiang, Changhua | Wang, Li | Wang, Chunhui

Correspondence: [*] Corresponding author. Bo Wang, National Key Laboratory of Human Factors Engineering, Astronaut Center of China, Beijing, P.R. China. Tel./Fax: +86 1066365690; E-mail: wowbob@139.com.

Abstract: To handle the randomness of individual judgments, a novel method, called uncertain rank correlation analysis (RCA), is proposed. Conventional RCA cannot effectively deal with the uncertainty in the comprehensive evaluation. However, normal distribution interval number is often used to express the uncertainty. Adopting normal cloud theory, this paper extends conventional RCA into the field of uncertainty, via the transformation and operation of the normal distribution interval-valued number. An example with uncertainties is given to verify the effectiveness of the proposed method.

Keywords: AHP, comprehensive evaluation, rank correlation, interval value, cloud theory

DOI: 10.3233/JIFS-169568

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 69-74, 2018