

Conference: 

Conference: 

Tutorial: 



Special Session: 

Special Session: 

Special Session: 

Special Session: 

Conference: First, it is shown that information granularity is of paramount relevance in building linkages between realworld data and symbols encountered in AI processing. Second, we stress that a suitable level of abstraction (specificity of information granularity) becomes essential to support useroriented framework of design and functioning AI artifacts. In both cases, central to all pursuits is a process of formation of information granules and their prudent characterization. We discuss a comprehensive approach to the development of information granules by means of the principle of justifiable granularity. Here various construction scenarios are discussed including those engaging conditioning and collaborative mechanisms incorporated in the design of information granules. The mechanisms of assessing the quality of granules are presented. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the AI constructs. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data. With this regard, selected aspects of stability and summarization of symbol oriented information are discussed. 
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Conference: In the talk I will demonstrate how the idea of working with chunks of data that are bound by indiscernibility and/or similarity translates to novel approaches to some key tasks in Machine Learning. Using practical examples drawn from some of the recently initiated R&D projects as a vehicle I will show possible applications in knowledge discovery from data including tagging/labelling, creation of explainable classifiers, grouping and clustering, and adaptive learning. I will discuss the possible gains that similaritybased approach can bring in terms of both the quality of discovered knowledge and reduction of time and effort required to acquire it. 
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Tutorial: The following topics will be covered in the tutorial:
References 1. Lin, T.Y.: Granular Computing1: The Concept of Granulation and Its Formal Model. Int. J. Granular Computing, Rough Sets and Int Systems 1(1) (2009) 21–42 2. Lin, T.Y.: Neighbourhood Systems Applications To Qualitative Fuzzy and Rough Sets. In Wang, P.P., et al., eds.: Advances in Machine Intelligence and Soft Computing’ Duke University, Durham’1997. (1997) 132–155 3. Zadeh, L.A.: Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90(2) (1997) 111– 127 4. Lin, T.Y.: Granular Fuzzy Sets: A View From Rough Set and Probability Theories. International Journal of Fuzzy Systems 3(2) (2001) 373–381 5. Mani, A.: Dialectics of Counting and The Mathematics of Vagueness. Transactions on Rough Sets XV(LNCS 7255) (2012) 122–180 6. Mani, A.: Algebraic Methods for Granular Rough Sets. In Mani, A., Düntsch, I., Cattaneo, G., eds.: Algebraic Methods in General Rough Sets. Trends in Mathematics. Birkhauser Basel (2018) 157–336 7. Mani, A.: Ontology, Rough YSystems and Dependence. Internat. J of Comp. Sci. and Appl. 11(2) (2014) 114–136 Special Issue of IJCSA on Computational Intelligence. 8. Mani, A.: High Granular Operator Spaces and LessContaminated General Rough Mereologies. Forthcoming (2019) 1–77 9. Mani, A., Düntsch, I., Cattaneo, G., eds. Trends in Mathematics. Birkhauser Basel (2018) 10. Yao, Y.Y., Zhang, N., Miao, D.: SetTheoretic Approaches To Granular Computing. Fundamenta Informaticae 115 (2012) 247–264 11. Yao, Y.Y.: The Art of Granular Computing. In Kryszkiewicz, M., et al., eds.: RSEISP’2007, LNAI 4585, Springer Verlag (2007) 101–112 12. Ciucci, D.: Orthopairs and Granular Computing . Granular Computing In Press (2016) 1–12 13. Mani, A.: Dialectical Rough Sets, Parthood and Figures of OppositionI. Transactions on Rough Sets XXI(LNCS 10810) (2018) 96–141 14. Sl¸ezak, D., Wasilewski, P.: Granular Sets  Foundations and Case Study of Tolerance Spaces. In An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G., eds.: RSFDGrC 2007, LNCS. Volume 4482. Springer (2007) 435–442 15. Mao, H., Hu, M., Yao, Y.Y.: Algebraic Approaches To Granular Computing. Granular Computing (2019) 1–13 16. Polkowski, L., SemeniukPolkowska, M.: Granular Rough Mereological Logics with Applications to Dependencies in Information and Decision Systems. Transactions on Rough Sets XII(LNCS 6190) (2010) 1–20 17. Pagliani, P., Chakraborty, M.: A Geometry of Approximation: Rough Set Theory: Logic, Algebra and Topology of Conceptual Patterns. Springer, Berlin (2008) 18. Banerjee, M., Yao, Y.Y.: Categorical Basis for Granular Computing. In Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G., eds.: RSFDGrC 2007, LNAI. Springer (2007) 427–434 19. Mani, A.: Algebraic Semantics of ProtoTransitive Rough Sets. Transactions on Rough Sets XX(LNCS 10020) (2016) 51–108 20. Mani, A.: On Deductive Systems of AC Semantics for Rough Sets. ArXiv. Math (1610.02634v1) (October 2016) 1–12 21. Keet, C.M.: A Formal Theory of Granules  Phd Thesis. PhD thesis, Fac of Comp.Sci., Free University of Bozen (2008) 22. Burkhardt, H., Seibt, J., Imaguire, G., Gerogiorgakis, S., eds.: Handbook of Mereology. Philosophia Verlag, Germany (2017) 23. Potochnik, A., McGill, B.: The Limitations of Hierarchical Organization. Philosophy of Science 79 (2012) 12–140 24. Cotnoir, A.J., Varzi, A.C.: Natural Problems for Classical Mereology. The Review of Symbolic Logic 12(1) (2019) 201–208 25. Mani, A.: Rough Contact in General Rough Mereology. In de Paiva, V., Brigitte, P., Amy, F., eds.: Proceedings of Women in Logic Workshop, Vancouver: LICS 2019. (june 2019) 1–10 26. Skowron, A., Jankowski, A.: Rough Sets and Interactive Granular Computing. Fundamenta Informaticae 147 (2016) 371–385 27. Skowron, A., Jankowski, A., Dutta, S.: Interactive granular computing. Granular Computing 1(2) (2016) 95–113 
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Tutorial: In the past couple of years, fuzzy rough sets have been used in machine learning in many different ways. The python library fuzzyroughlearn implements some of the most useful proposals, and is compatible with scikitlearn, the goto general purpose machine learning library in python. This tutorial will cover the application of the following classification models:

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Special session: The session should also allow the attendees of IJCRS to make a significant update of their knowledge about current trends in rough sets and data science. We hope that this session will provide a means for better exchange of scientific ideas and deeper integration of the rough set community. For obvious legal reasons, the papers accepted for the session will not be published in the IJCRS proceedings, however the authors are encourage to share with the IJCRS organisers their presentations/slides so as to make them available (after the conference) to all IJCRS attendees and spread the "news". We would also like to have a look at the opposite direction in time and organise a subsession: Back to the Future: Incoming Publications Therefore very short papers, (24)page long, which are supposed to form a basis for future full articles, are also very welcome. The feedback from the IJCRS participants should allow authors to develop further their original ideas and submit the final version to leading scientific journals. We do hope that discussion of research which is conducted outside IJCRS will significantly enrich and popularise the premier rough set conference. The authors willing to contribute to this special session are asked to send the title and other bibliographic data of their already published paper or the extended abstract (24 pages long) of current research to:

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to be completed ... See other details in https://easychair.org/cfp/IJCRS2020 