Jerzy Grzymala-Busse – författare

This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.

This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.

This collection of articles is devoted to fuzzy as well as rough set theories. Both theoriesarebasedonrigorousideas,methodsandtechniquesinlogic,mathem- ics, and computer science for treating problems for which approximate solutions are possible only, due to their inherent ambiguity, vagueness, incompleteness, etc. Vast areas of decision making, data mining, knowledge discovery in data, approximatereasoning,etc., aresuccessfully exploredusing methods workedout within fuzzy and rough paradigms. By the very nature of fuzzy and rough paradigms, outlined above, they are related to distinct logical schemes: it is well-known that rough sets are related to modal logicsS5andS4(Orl owska, E., Modal logics in the theory of infor- tion systems, Z. Math. Logik Grund. Math. 30, 1984, pp. 213 ?.; Vakarelov, D., Modal logics for knowledgerepresentationsystems,LNCS 363,1989,pp. 257?.) and to ?nitely-valued logics (Pagliani, P., Rough set theory and logic-algebraic structures. In Incomplete Information: Rough Set Analysis,Orlo wska, E., ed., Physica/Springer, 1998, pp. 109 ?.; Polkowski, L. A note on 3-valued rough logic accepting decision rules, Fundamenta Informaticae 61, to appear).Fuzzy sets are related to in?nitely-valued logics (fuzzy membership to degree r? [0,1]expressingtruthdegreer)(Goguen,J.A.,Thelogicofinexactconcepts, Synthese18/19,1968-9,pp.325?.;Pavelka,J.,OnfuzzylogicI,II,III,Z. Math. Logik Grund. Math. 25, 1979, pp. 45 ?., pp. 119 ?., pp. 454 ?.; Dubois, D., Prade, H., Possibility Theory, Plenum Press, 1988; Haj ' ek, P., Metamathematics of Fuzzy Logic, Kluwer, 1998).

This volume of the Transactions on Rough Sets commemorates the life and work of Zdzislaw Pawlak (1926-2006), whose legacy is rich and varied. It presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak, introducing a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science.