Abstract
In the context of computing and informatics, Cognitive Diversity (CD) has been proposed to characterize the degree of dissimilarity between multiple scoring systems (MSS). As such, CD serves a role in informatics analogous to that of Pearson’s Correlation in classical statistics. Here we review MSS and explore CD’s utility in relation to the notions of correlation and distance in machine learning, ensemble methods, rank aggregation, and combinatorial fusion in both parametric score space and non-parametric rank space. Finally, we survey applications of CD in combining MSS in a variety of domains in science, technology, society, business, and management. Our study provides a new data science framework for discovery in data-rich environments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
