An improvement of rough sets’ accuracy measure using containment neighborhoods with a medical application
Information Sciences, ISSN: 0020-0255, Vol: 569, Page: 110-124
2021
- 109Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
The rough set theory is a nonstatistical mathematical approach to address the issues of vagueness and uncertain knowledge. The rationale of this theory relies on associating a subset with two crisp sets called lower and upper approximations which are utilized to determine the boundary region and accuracy measure of that subset. Neighborhoods systems are pivotal technique to reduce the boundary region and improve the accuracy measure. Therefore, we aim through this paper to introduce new types of neighborhoods called containment neighborhoods (briefly, Cj -neighborhoods). They are defined depending on the inclusion relations between j -neighborhoods under arbitrary binary relation. We study their relationships with some previous types of neighborhoods, and determine the conditions under which they are equivalent. Then, we applied Cj -neighborhoods to present the concepts of Cj -lower and Cj -upper approximations and reveal main properties with the help of examples. We also prove that a Cj -accuracy measure is the highest in cases of j=i,〈i〉. Furthermore, we compare our approach with two approaches given in published literatures and show that accuracy measure induced from our technique is the best. Finally, we successfully applied Cj -neighborhoods, Nj -neighborhoods and Ej -neighborhoods in a medical application aiming to classify medical staff in terms of suspected infection with the new corona-virus (COVID-19).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0020025521003376; http://dx.doi.org/10.1016/j.ins.2021.04.016; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85104675052&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0020025521003376; https://dx.doi.org/10.1016/j.ins.2021.04.016
Elsevier BV
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