Abstract

Conflict analysis plays an important role in the fields of politics, military operations, economics, business management, games, urban planning, management negotiations and etcetera. Computational intelligence model such as rough set theory has been used in managing conflict situations which have the ability to handle uncertainties. However, there is a great concern in the computational time of the rough set approach in determining strength, certainty and coverage of conflicts. Motivated by the fact that every rough set approach can be represented using soft set theory, we derived an alternative method based on the concept of co-occurrence from multi-soft sets to handle conflict situations. We first used an illustrative example of a movie selection problem to demonstrate the proposed approach and provide an extensive elaboration using a publicly available dataset. Our motivation is to provide a new measure based on support, strength, certainty and coverage of soft set on movie selection problem. Our findings have revealed that the proposed approach achieved less computational time when compared with the rough set-based approach of up to 8.05%. One potential application of the proposed approach is the domain of recommendation systems. The proposed approach can be used to easily identify users/items nearest neighbours based on support, strength, certainty and coverage, which is crucial for the success of recommendation systems.

Full Text
Paper version not known

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 call

Disclaimer: 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.