Estimation of raw silk quality using rough set theory

• Published in: Journal of the Textile Institute Vol. 113; no. 11; pp. 2352 - 2357
• Main Authors: Kar, Niharendu Bikash, Ghosh, Anindya, Das, Subhasis, Banerjee, Debamalya
• Format: Journal Article
• Language: English
• Published: Manchester Taylor & Francis 26-10-2022; Taylor & Francis Ltd
• Subjects: Approximation; cocoon; Datasets; Decision theory; Natural products; Parameters; raw silk; reduct; rough set; rule generation; Set theory; Silk
• ISSN: 0040-5000; 1754-2340
• DOI: 10.1080/00405000.2021.1983963

Being an agro based natural product, silk always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of raw silk quality from the imprecise cocoon parameters is an intricate problem. Rough set theory has evolved as one of the most important technique used for handling imprecise data. One of the cardinal uses of rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for the applications of rough set theory. Our approach focuses on the elimination of the redundant data set in order to generate the effective decision rule which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict renditta from five cocoon parameters such as shell ratio, defective cocoon percentage, cocoon volume, cocoon weight and filament length.