Rough sets: Some extensions

In this article, we present some extensions of the rough set approach and we outline a challenge for the rough set based research.

[1]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[2]  Lotfi A. Zadeh,et al.  A New Direction in AI: Toward a Computational Theory of Perceptions , 2001, AI Mag..

[3]  Ning Zhong,et al.  Intelligent Technologies for Information Analysis , 2004, Springer Berlin Heidelberg.

[4]  Lech Polkowski,et al.  Rough Mereology: A Rough Set Paradigm for Unifying Rough Set Theory and Fuzzy Set Theory , 2003, Fundam. Informaticae.

[5]  G. Mann The Quark and the Jaguar: adventures in the simple and the complex , 1994 .

[6]  Andrzej Skowron,et al.  Rough mereology: A new paradigm for approximate reasoning , 1996, Int. J. Approx. Reason..

[7]  Stephen Read,et al.  Thinking about logic : an introduction to the philosophy oflogic , 1995 .

[8]  Andrzej Skowron,et al.  Rough Sets and Vague Concepts , 2004, Fundam. Informaticae.

[9]  James J. Alpigini,et al.  Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing , 2002 .

[10]  L. Barsalou,et al.  Whither structured representation? , 1999, Behavioral and Brain Sciences.

[11]  James F. Peters,et al.  Rough Sets: Trends and Challenges , 2003, RSFDGrC.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Jerzy Stefanowski,et al.  Hyperplane Aggregation of Dominance Decision Rules , 2003, Fundam. Informaticae.

[14]  Steffen Staab,et al.  Handbook on Ontologies (International Handbooks on Information Systems) , 2004 .

[15]  Ivo Düntsch,et al.  Rough set data analysis: A road to non-invasive knowledge discovery , 2000 .

[16]  Andrzej Skowron,et al.  Rough mereology in information systems. A case study: qualitative spatial reasoning , 2000 .

[17]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[18]  Andrzej Skowron,et al.  Approximation Spaces and Information Granulation , 2004, Trans. Rough Sets.

[19]  Salvatore Greco,et al.  Incremental versus Non-incremental Rule Induction for Multicriteria Classification , 2004, Trans. Rough Sets.

[20]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

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

[22]  D. Vanderpooten Similarity Relation as a Basis for Rough Approximations , 1995 .

[23]  Masahiro Inuiguchi,et al.  Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science) , 2005 .

[24]  Andrzej Skowron,et al.  Rough-Neural Computing: Techniques for Computing with Words , 2004, Cognitive Technologies.

[25]  J. Sutherland The Quark and the Jaguar , 1994 .

[26]  J. Stepaniuk Approximation Spaces, Reducts and Representatives , 1998 .

[27]  G. Leibniz Discourse on Metaphysics , 1902 .

[28]  Dominik Slezak,et al.  Feedforward Concept Networks , 2004, MSRAS.

[29]  William K. Bellinger Decision Rules , 2008, Encyclopedia of GIS.

[30]  Andrzej Skowron,et al.  Rough Mereological Calculi of Granules: A Rough Set Approach To Computation , 2001, Comput. Intell..

[31]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[32]  Rajkumar Roy,et al.  Advances in Soft Computing , 2018, Lecture Notes in Computer Science.

[33]  Yiyu Yao,et al.  Information granulation and rough set approximation , 2001, Int. J. Intell. Syst..

[34]  Andrzej Skowron,et al.  Toward Intelligent Systems: Calculi of Information Granules , 2001, JSAI Workshops.

[35]  Andrzej Skowron,et al.  Rough set approach to pattern extraction from classifiers, In: Proceedings of the Workshop on Rough Sets in Knowledge Discovery and Soft Computing at ETAPS’2003 , 2003 .

[36]  Z. Pawlak Classification of objects by means of attributes , 1981 .

[37]  T. Poggio,et al.  The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.

[38]  E. H. Moore ON THE FOUNDATIONS OF MATHEMATICS. , 1903 .

[39]  Roman Słowiński,et al.  Dealing with Missing Data in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems , 2000 .

[41]  Jerzy W. Grzymala-Busse,et al.  Transactions on Rough Sets XII , 2010, Lecture Notes in Computer Science.

[42]  Steffen Staab,et al.  International Handbooks on Information Systems , 2013 .

[43]  Jan G. Bazan Behavioral Pattern Identification Through Rough Set Modeling , 2005, Fundam. Informaticae.

[44]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[45]  Andrzej Skowron,et al.  Approximate Reasoning in Distributed Environments , 2004 .

[46]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[47]  Andrzej Skowron,et al.  Ontological Framework for Approximation , 2005, RSFDGrC.

[48]  Philosophical Essays , 1997, Nature.

[49]  Tsau Young Lin,et al.  A Review of Rough Set Models , 1997 .

[50]  C. A. Murthy,et al.  Proceedings of the First international conference on Pattern Recognition and Machine Intelligence , 2005 .

[51]  J. Grzymala-Busse Managing uncertainty in expert systems , 1991 .

[52]  S. Poirier Foundations of mathematics , 2007 .

[53]  Andrzej Skowron,et al.  Rough sets: Trends and challenges (plenary talk) , 2003 .

[54]  Peter Stone,et al.  Layered learning in multiagent systems - a winning approach to robotic soccer , 2000, Intelligent robotics and autonomous agents.

[55]  Dominik Slezak,et al.  Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis , 2000, Fundam. Informaticae.

[56]  Janusz Kacprzyk,et al.  Computing with Words in Information/Intelligent Systems 1 , 1999 .

[57]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[58]  Andrzej Skowron,et al.  Information granules: Towards foundations of granular computing , 2001, Int. J. Intell. Syst..

[59]  Andrzej Skowron,et al.  Layered Learning for Concept Synthesis , 2004, Trans. Rough Sets.

[60]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[61]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[62]  D. Scott Perceptual learning. , 1974, Queen's nursing journal.

[63]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[64]  F. Ramsey The foundations of mathematics , 1932 .

[65]  Georgios I. Doukidis,et al.  Decision making : recent developments and worldwide applications , 2000 .

[66]  Andrzej Skowron,et al.  Rough sets in perception-based computing (keynote talk) , 2005 .

[67]  Dominik Slezak,et al.  The investigation of the Bayesian rough set model , 2005, Int. J. Approx. Reason..

[68]  Wojciech Ziarko,et al.  The Discovery, Analysis, and Representation of Data Dependencies in Databases , 1991, Knowledge Discovery in Databases.

[69]  S. Greco,et al.  Data mining tasks and methods: Classification: multicriteria classification , 2002 .

[70]  S. Harnad Categorical Perception: The Groundwork of Cognition , 1990 .

[71]  Dominik Slezak,et al.  Rough Sets and Bayes Factor , 2005, Trans. Rough Sets.

[72]  S. Greco,et al.  Dominance-Based Rough Set Approach to Knowledge Discovery (II): Extensions and Applications , 2004 .

[73]  Hung Son Nguyen,et al.  A View on Rough Set Concept Approximations , 2003, Fundam. Informaticae.

[74]  Sven Behnke,et al.  Hierarchical Neural Networks for Image Interpretation , 2003, Lecture Notes in Computer Science.

[75]  Salvatore Greco,et al.  Rough Set Analysis of Preference-Ordered Data , 2002, Rough Sets and Current Trends in Computing.

[76]  R. Keefe Theories of vagueness , 2000 .

[77]  Andrzej Skowron,et al.  Tolerance Approximation Spaces , 1996, Fundam. Informaticae.

[78]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[79]  Andrzej Skowron,et al.  Perception logic in intelligent systems (keynote talk) , 2005 .

[80]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[81]  Tuan Trung Nguyen,et al.  Rough Set Approach to Domain Knowledge Approximation , 2003, Fundam. Informaticae.

[82]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[83]  James F. Peters,et al.  Rough Set Approach to Pattern Extraction from Classifiers , 2003, RSKD.

[84]  S. Leśniewski Grundzüge eines neuen Systems der Grundlagen der Mathematik , 1929 .

[85]  Solomon Marcus,et al.  The Paradox of the Heap of Grains in Respect to Roughness, Fuzziness and Negligibility , 1998, Rough Sets and Current Trends in Computing.

[86]  Jan G. Bazan,et al.  Rough set algorithms in classification problem , 2000 .

[87]  Dominik Ślęzak,et al.  Various approaches to reasoning with frequency based decision reducts: a survey , 2000 .

[88]  Sudarsan Nanda,et al.  On rough relations , 1998 .

[89]  A. Skowron,et al.  Towards adaptive calculus of granules , 1998 .

[90]  Munindar P. Singh,et al.  Readings in agents , 1997 .

[91]  Salvatore Greco,et al.  Fuzzy Similarity Relation as a Basis for Rough Approximations , 1998, Rough Sets and Current Trends in Computing.

[92]  Lech Polkowski,et al.  Toward Rough Set Foundations. Mereological Approach , 2004, Rough Sets and Current Trends in Computing.

[93]  Andrzej Skowron,et al.  Complex Patterns , 2003, Fundam. Informaticae.

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

[95]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[96]  Zdzislaw Pawlak,et al.  Decision Rules, Bayes' Rule and Ruogh Sets , 1999, RSFDGrC.

[97]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[98]  Andrzej Skowron,et al.  Classifiers Based on Approximate Reasoning Schemes , 2004, MSRAS.

[99]  S. Tsumoto,et al.  Rough set methods and applications: new developments in knowledge discovery in information systems , 2000 .

[100]  Andrzej Skowron,et al.  Information Granules and Rough-Neural Computing , 2004 .

[101]  Andrzej Skowron,et al.  On-Line Elimination of Non-relevant Parts of Complex Objects in Behavioral Pattern Identification , 2005, PReMI.

[102]  Andrzej Skowron,et al.  Rough Sets and Higher Order Vagueness , 2005, RSFDGrC.

[103]  Andrzej Skowron,et al.  Monitoring, Security, and Rescue Techniques in Multiagent Systems (Advances in Soft Computing) , 2005 .

[104]  S. Greco,et al.  Dominance-Based Rough Set Approach to Knowledge Discovery (I): General Perspective , 2004 .