The two sides of the theory of rough sets

There exist two formulations of the theory of rough sets. A conceptual formulation emphasizes on the meaning and interpretation of the concepts and notions of the theory, whereas a computational formulation focuses on procedures and algorithms for constructing these notions. Except for a few earlier studies, computational formulations dominate research in rough sets. In this paper, we argue that an oversight of conceptual formulations makes an in-depth understanding of rough set theory very difficult. The conceptual and computational formulations are the two sides of the same coin; it is essential to pay equal, if not more, attention to conceptual formulations. As a demonstration, we examine and compare conceptual and computational formulations of two fundamental concepts of rough sets, namely, approximations and reducts.

[1]  Mansoor Niaz,et al.  Relationship between student performance on conceptual and computational problems of chemical equilibrium , 1995 .

[2]  Yiyu Yao,et al.  On Generalizing Rough Set Theory , 2003, RSFDGrC.

[3]  Decui Liang,et al.  Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets , 2014, Inf. Sci..

[4]  Duoqian Miao,et al.  Reduction target structure-based hierarchical attribute reduction for two-category decision-theoretic rough sets , 2014, Inf. Sci..

[5]  Victor W. Marek,et al.  Contributions to the Theory of Rough Sets , 1999, Fundam. Informaticae.

[6]  Fan Min,et al.  A Three-way Decision Approach to Incremental Frequent Itemsets Mining , 2014 .

[7]  Mohua Banerjee,et al.  Rough Sets: Some Foundational Issues , 2013, Fundam. Informaticae.

[8]  Wang Ju,et al.  Reduction algorithms based on discernibility matrix: The ordered attributes method , 2001, Journal of Computer Science and Technology.

[9]  S. D. Lima,et al.  Concept or computation: Students' understanding of the mean , 1981 .

[10]  Jing Liu,et al.  An Integrated Method for Micro-blog Subjective Sentence Identification Based on Three-Way Decisions and Naive Bayes , 2014, RSKT.

[11]  Decui Liang,et al.  Incorporating logistic regression to decision-theoretic rough sets for classifications , 2014, Int. J. Approx. Reason..

[12]  Davide Ciucci Orthopairs in the 1960s: Historical Remarks and New Ideas , 2014, RSCTC.

[13]  Yiyu Yao,et al.  On Reduct Construction Algorithms , 2006, RSKT.

[14]  Bing Zhou,et al.  Multi-class decision-theoretic rough sets , 2014, Int. J. Approx. Reason..

[15]  Ewa Orlowska,et al.  Expressive Power of Knowledge Representation Systems , 1984, Int. J. Man Mach. Stud..

[16]  Patrick J. Hurley,et al.  A Concise Introduction to Logic (with CD-ROM) (Concise Introduction to Logic) , 2005 .

[18]  Zdzislaw Pawlak,et al.  Information systems theoretical foundations , 1981, Inf. Syst..

[19]  Bruce Roberts,et al.  Charlotte, North Carolina , 1958 .

[20]  Y. Yao Information granulation and rough set approximation , 2001 .

[21]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[22]  Nouman Azam,et al.  Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets , 2014, Int. J. Approx. Reason..

[23]  Yiyu Yao Rough Set Approximations : a Concept Analysis Point Of , 2013 .

[24]  李华雄,et al.  Cost-Sensitive Three-Way Decision : A Sequential Strategy , 2013 .

[25]  Yiyu Yao,et al.  A Note on Definability and Approximations , 2007, Trans. Rough Sets.

[26]  Victor W. Marek,et al.  Information Storage and Retrieval Systems: Mathematical Foundations , 1976, Theor. Comput. Sci..

[27]  Zhenmin Tang,et al.  Minimum cost attribute reduction in decision-theoretic rough set models , 2013, Inf. Sci..

[28]  Didier Dubois,et al.  A map of dependencies among three-valued logics , 2013, Inf. Sci..

[29]  J. Watt,et al.  Research Methods for Communication Science , 1995 .

[30]  Huaxiong Li,et al.  A Method to Reduce Boundary Regions in Three-Way Decision Theory , 2014, RSKT.

[31]  William Rybolt,et al.  Conceptual versus Computational Formulae in Calculus and Statistics Courses , 2012 .

[32]  Jingtao Yao,et al.  Decision-theoretic rough sets and beyond , 2014, International Journal of Approximate Reasoning.

[33]  Da Ruan,et al.  Probabilistic model criteria with decision-theoretic rough sets , 2011, Inf. Sci..

[34]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[35]  Dominik Slezak,et al.  Association Reducts: Complexity and Heuristics , 2006, RSCTC.

[36]  Zhenmin Tang,et al.  On an optimization representation of decision-theoretic rough set model , 2014, Int. J. Approx. Reason..

[37]  Yiyu Yao,et al.  The Concept of Reducts in Pawlak Three-Step Rough Set Analysis , 2013, Trans. Rough Sets.

[38]  Jerzy W. Grzymala-Busse,et al.  Knowledge acquisition under uncertainty — a rough set approach , 1988, J. Intell. Robotic Syst..

[39]  John F. Sowa,et al.  Conceptual Structures: Information Processing in Mind and Machine , 1983 .

[40]  Baoli Wang,et al.  A Novel Intelligent Multi-attribute Three-Way Group Sorting Method Based on Dempster-Shafer Theory , 2014, RSKT.

[41]  Ryszard S. Michalski,et al.  Categories and Concepts: Theoretical Views and Inductive Data Analysis , 1993 .

[42]  Yiyu Yao,et al.  Relational Interpretations of Neigborhood Operators and Rough Set Approximation Operators , 1998, Inf. Sci..

[43]  I. Saleh,et al.  Contemporary science teaching approaches : promoting conceptual understanding in science , 2012 .

[44]  Guoyin Wang,et al.  An automatic method to determine the number of clusters using decision-theoretic rough set , 2014, Int. J. Approx. Reason..

[45]  Edward E. Smith Concepts and induction , 1989 .

[46]  Guoyin Wang,et al.  Decision region distribution preservation reduction in decision-theoretic rough set model , 2014, Inf. Sci..

[47]  M. Posner Foundations of cognitive science , 1989 .

[48]  Jiye Liang,et al.  Uncertainty and Feature Selection in Rough Set Theory , 2011, RSKT.

[49]  Victor W. Marek Zdzisław Pawlak, Databases and Rough Sets , 2013, Rough Sets and Intelligent Systems.

[50]  Nouman Azam,et al.  Game-theoretic rough sets for recommender systems , 2014, Knowl. Based Syst..

[51]  Liping Ma,et al.  Knowing and Teaching Elementary Mathematics Teachers' Understanding of Fundamental Mathematics in China and the United States , 2010 .

[52]  Jiye Liang,et al.  International Journal of Approximate Reasoning Multigranulation Decision-theoretic Rough Sets , 2022 .

[53]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[54]  Hamido Fujita,et al.  Evidential Probabilities for Rough Set in a Case of Competitiveness , 2014, KSE.

[55]  P. Hurley A concise introduction to logic , 1982 .

[56]  Yanhong She On Determination of Thresholds in Three-Way Approximation of Many-Valued NM-Logic , 2014, RSCTC.

[57]  I. A. Richards,et al.  The Meaning of Meaning: a Study of the Influence of Language upon Thought and of the Science of Symbolism , 1923, Nature.

[58]  Julius T. Tou,et al.  Information Systems , 1973, GI Jahrestagung.

[59]  Tianrui Li,et al.  Dynamic Maintenance of Three-Way Decision Rules , 2014, RSKT.

[60]  Yiyu Yao,et al.  Decision-theoretic three-way approximations of fuzzy sets , 2014, Inf. Sci..

[61]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[62]  Zdzisław Pawlak Mathematical foundations of information retrieval systems , 1973 .

[63]  Richard A. Rhodes,et al.  Conceptual Physics: Matter in Motion , 1971 .

[64]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[65]  Bao Qing Hu,et al.  Three-way decisions space and three-way decisions , 2014, Inf. Sci..

[66]  Zbigniew Bonikowski,et al.  A Certain Conception of the Calculus of Rough Sets , 1992, Notre Dame J. Formal Log..