The probe for the weighted dual probabilistic linguistic correlation coefficient to invest an artificial intelligence pr
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METHODOLOGIES AND APPLICATION
The probe for the weighted dual probabilistic linguistic correlation coefficient to invest an artificial intelligence project Wanying Xie1,3 • Zeshui Xu1,2 • Zhiliang Ren1 • Enrique Herrera-Viedma3,4
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract As one of the burgeoning decision-making instruments, the integrity of dual probabilistic linguistic term sets (DPLTSs) is to express the decision information in terms of cognitive certainty and uncertainty. The superiority of correlation coefficient is to demonstrate the interrelationship of the variables. This paper aims to give full play to the advantages of the above two. Firstly, it defines the dual probabilistic linguistic correlation coefficient. Then, it is based on the proposed entropy for DPLTSs calculates the comprehensive weight vector. Moreover, combined with the proposed correlation coefficient, it further defines the weighted correlation coefficient as a measure for the application about artificial intelligence. Besides, it uses the dual probabilistic linguistic closeness coefficient as the reference to compare the pros and cons. Finally, a specific numeric simulation is utilized to demonstrate the feasibility of the two different measures. Keywords Dual probabilistic linguistic term sets Weighted correlation coefficient Distance measure Closeness coefficient Multi-attribute group decision making
1 Introduction
Communicated by V. Loia. & Zeshui Xu [email protected] & Enrique Herrera-Viedma [email protected] Wanying Xie [email protected] Zhiliang Ren [email protected] 1
School of Economics and Management, Southeast University, Nanjing 211189, Jiangsu, China
2
Business School, Sichuan University, Chengdu 610064, Sichuan, China
3
Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain
4
Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Since the computer ‘‘dark blue’’ of the IBM company defeated the world chess champion of mankind in 2016, AI has aroused the extensive attention of the public. Actually, with the technological breakthrough of AI industry and its continuous integration with traditional industries, AI plays a huge role in promoting the transformation and upgrading of industries and improving production efficiency (Wang 2018). Moreover, its related technology has permeated many areas, such as economic (Heinen et al. 2017; Agrawal et al. 2018; Egidi 2018; Garcı´a-Alonso et al. 2012), culture (Johnson and Valente 2009; Chen 2017), education (Mcarthur et al. 2005; Yoon and Kim 2017; Gadanidis 2017), and medical treatment (Gillan et al. 2018; Fan et al. 2018). Therefore, many scientific and technical corporations have begun to join the AI market war in succession. In order to better develop the AI industry, before determining the specific project, they need to do the evaluation for
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