Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mishra, Aneesh Kumar; Jain, Neelesh Kumar; Singh, Ravindra Kumar Title A name given to the resource An Intuitionistic Fuzzy-Rough Attribute Selection Using Representative Samples Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Multiple-Valued Logic and Soft Computing;Volume;45;Issue;4;pp.439-463 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105017962593?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017962593?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mishra A.K., Department of Computer Science Engineering, Jaypee University of Engineering &amp; Technology, M.P., Guna, India; Jain N.K., Department of Computer Science Engineering, Jaypee University of Engineering &amp; Technology, M.P., Guna, India; Singh R.K., Electronics and Communication Engineering, CHRIST University, Karnataka, Bangalore, India Description An account of the resource Selecting relevant features is an important tool for extracting knowledge from datasets with many attributes and objects. The traditional theory of rough set is a fundamental and successful tool for dealing with vagueness and inconsistency. Combining the rough set with the fuzzy set handles the information loss problem arising from the discretisation process. Still, it fails to consider the hesitancy part of any information system. A generalisation of fuzzy set known as an intuitionistic fuzzy (IF) set has more real-world applications to confront uncertainty and ambiguity than the fuzzy set. So, the combination of rough set and IF set not only deals with vagueness but also able to consider the hesitancy available in any real-world data. In this work, we propose an IF rough set model based on representative samples and its application in the area of attribute reduction of high-dimensional datasets. First, we defined the representative sample-based intuitionistic fuzzy rough set and then presented an algorithm to calculate the reduction of a dataset using the degree of dependency method. Mathematical theorems are applied to validate the presented model theoretically. Experimental analysis is also discussed to validate the proposed technique. Finally, we applied our proposed method to improve the prediction of antifungal peptides. 2025 Old City Publishing, Inc. Subject The topic of the resource Attribute reduction; decision making; intuitionistic fuzzy rough set; intuitionistic fuzzy set; representative sample; rough set Publisher An entity responsible for making the resource available Old City Publishing Relation A related resource ISSN: 15423980; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online