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Soft covering based rough sets and their application
Şaziye Yüksel1, Zehra Güzel Ergül2, Naime Tozlu3
1Department of Mathematics, Faculty of Science, Selçuk University, 42003 Konya, Turkey.
Thescientificworldjournal
|October 9, 2014
Summary
This study introduces novel soft covering rough sets, a hybrid model combining soft sets and rough sets. This new model refines approximation operators and shows potential in medical applications like identifying prostate cancer risk.
Area of Science:
- Data Science
- Artificial Intelligence
- Medical Informatics
Background:
- Soft rough sets integrate rough sets and soft sets, offering a generalized rough set model.
- Existing models lack the precision for complex decision-making in specialized fields like medicine.
Purpose of the Study:
- To introduce a novel soft covering rough set model by integrating covering soft sets with rough sets.
- To establish a soft covering approximation space and define new approximation operators.
- To analyze the properties of these new operators and their relationship to existing ones.
Main Methods:
- Development of soft covering rough approximation operators.
- Theoretical analysis of the properties of the new operators.
- Application of the model to a medical dataset for prostate cancer risk assessment.
Main Results:
- The proposed soft covering upper approximation operator is shown to be smaller than the standard soft upper approximation operator.
- The new model provides a refined approach to approximation in soft set theory.
- Demonstrated utility in a medical case study involving patient data.
Conclusions:
- The novel soft covering rough set model offers enhanced precision in approximation.
- This framework has practical implications for medical data analysis, particularly in risk stratification.
- Further research can explore broader applications of this generalized model.
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