Rough set theory-based multi-class decision-making framework for cost-effective treatment

Sandip Majumder and Samarjit Kar ()
Additional contact information
Sandip Majumder: National Institute of Technology Durgapur
Samarjit Kar: National Institute of Technology Durgapur

OPSEARCH, 2025, vol. 62, issue 3, No 24, 1668-1685

Abstract: Abstract This paper provides a novel formulation of multiclass decision-making with the help of rough set theory. Rough set theory approximates a concept by the three regions, namely positive, negative, and boundary regions. The three regions enable us to derive three types of decisions, namely acceptance, rejection, and deferment. The deferment decision allows us to examine suspicious objects further and reduces the chance of misclassification. Different types of misclassification errors are treated separately based on the notation of loss function from Bayesian decision theory. In our cost-sensitive classification approach, the costs caused by different kinds of error are not assumed to be equal. The main object of this paper is to provide a direction for cost-effective treatment for a patient suspected to be affected by a significant disease. A numerical example of cost-effective treatment for a patient suspected to be affected by a significant disease demonstrates the practicability and efficacy of the developed idea in real-life applications.

Keywords: Rough set; Rough membership function; Probabilistic rough set; Decision-theoretic rough; Bayesian decision procedure (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12597-024-00860-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:62:y:2025:i:3:d:10.1007_s12597-024-00860-3

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-024-00860-3

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().