International Joint Conference on Rough Sets

La Habana, June 29 - July 3, 2020

Welcome

Aim and Scope

Submission

Registration

Important dates

Organizing Committee

Local Organizers

Program

 

Preliminary Program

Conference: Explainable AI: From Data to Symbols and Information Granules

Witold Pedrycz [Invited speaker]

Conference: How indiscernibility and similarity make life easier

Dominik Ślęzak [Invited speaker]

Tutorial: Comparative approaches to granularity in general rough sets

A. Mani

Tutorial: Fuzzy rough set classification techniques

Oliver Urs Lenz

Special Session: Fuzzy Logic, Formal Concept Analysis and Rough Sets

María Eugenia Cornejo
Dominik Ślęzak
Eloisa Ramírez-Poussa

Special Session: Fuzzy and Rough Cognitive Networks

Gonzalo Nápoles,
László Kóczy

Special Session: Rough sets and Matroids

Mauricio Restrepo

Special Session: Review of the Year. A Look back at Rough Sets

Davide Ciucci
Marcin Wolski

Preliminary details





Conference: Explainable AI: From Data to Symbols and Information Granules by Witold Pedrycz [Invited speaker]

With the progress and omnipresence of Artificial Intelligence (AI), two aspects of this discipline become more and more apparent. When tackling with some important societal underpinnings, especially those encountered in strategic areas, AI constructs call for higher explainability capabilities. Some of the recent advancements in AI fall under the umbrella of industrial developments (which are predominantly driven by numeric data). With the vast amounts of data, one needs to resort herself to engaging abstract entities in order to cope with complexity of the real-world problems and delivers transparency of the required solutions. All of those factors give rise to a recently pursued discipline of explainable AI (XAI). From the dawn of AI, symbols and ensuing symbolic process have assumed a central position and ways of symbol grounding become of interest. We advocate that in the realization of the two timely pursuits of XAI, information granules and Granular Computing (embracing fuzzy sets, rough sets, intervals, among others) play a significant role. The two profound features that facilitate explanation and interpretation are about an accommodation of the logic fabric of constructs and a selection of a suitable level of abstraction. They go hand-in-hand with the information granules.
First, it is shown that information granularity is of paramount relevance in building linkages between real-world data and symbols encountered in AI processing. Second, we stress that a suitable level of abstraction (specificity of information granularity) becomes essential to support user-oriented 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: How indiscernibility and similarity make life easier by Dominik Ślęzak [Invited speaker]

Nowadays we are flooded by data. This becomes a problem when we need to make sense of what is in it. The sheer volume, veracity and variety of data sets, notwithstanding the complexity of concepts we want to discover, poses a challenge. But, what if we could mitigate the problems with width, breadth and depth of data sets by making use of the most fundamental concepts underlying the rough set approach - indiscernibility and similarity. What if we could devise an approach to granulating the data in such the way that it becomes more manageable and interpretable.
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 similarity-based 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: Comparative approaches to granularity in general rough sets by A. Mani (HBCSE, Tata Institute of Fundamental Research, India)

A number of nonequivalent perspectives on granular computing are known in the literature, and many are in states of continuous development. Further related concepts of granules and granulations may be incompatible in many senses. The tutorial is intended to explain basic aspects of these from a use-based critical perspective, their range of applications and future directions relative to general rough sets and related formal approaches to vagueness. Methods of relating these concepts of granules relative to knowledge will also be part of the tutorial.
The following topics will be covered in the tutorial:
  • Overview of different concepts of granules and granulations. Related examples from the literature [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18].
  • Primitive, precision-based, axiomatic [6,19,20,8,7] and ontology based concepts of granules. Which approximations are granular?
  • Granulation from the perspective of application domains such as knowledge representation, modeling of human reasoning, databases, big data, and decision making: How to decide on the most appropriate?
  • Types, ontology, imprecise granules and taxonomy: examples from biology [21,22,23]
  • Mereology, High granular operator spaces and variants[22,24,8,25]
  • Problems of granular computing, hybrid contexts, and adaptivity[26,27]
Most of the focus will be on knowledge representation, ontology, mereology, types and modeling of human reasoning. Some stress will also be placed on conflicting views of granules used in the literature. A supporting survey article by the present author will be part of the tutorial. Despite her clear preference for an axiomatic approach in mereological setting, the expository article will be comprehensive and balanced.
References
1. Lin, T.Y.: Granular Computing-1: 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 Y-Systems 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 Less-Contaminated 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.: Set-Theoretic 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 Opposition-I. 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., Semeniuk-Polkowska, 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 Proto-Transitive 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: Fuzzy rough set classification techniques by Oliver Urs Lenz (Ghent University, Belgium)

Fuzzy rough sets allow us to apply the techniques of rough set theory to numerical data without going through discretisation. In particular, they provide a way to approximate concepts in a feature space based on the presence of positive and negative information. This makes fuzzy rough sets useful for machine learning, in which the objective is to learn generalised concepts with finite samples of data.
In the past couple of years, fuzzy rough sets have been used in machine learning in many different ways. The python library fuzzy-rough-learn implements some of the most useful proposals, and is compatible with scikit-learn, the go-to general purpose machine learning library in python.
This tutorial will cover the application of the following classification models:
  • Fuzzy Rough Nearest Neighbours (FRNN): a classification method that uses the nearest neighbours of an unseen instance to determine its membership in the upper and lower approximations of the decision classes, and classifies it accordingly.
  • FROVOCO: an adaptation of FRNN for imbalanced multi-class classification. It is an ensemble classifier that combines one-vs-one classification scores with the global affinity of unseen instances to the varying decision classes.
  • FRONEC: a model that uses fuzzy rough sets for multi-label classification, by selecting a label set on the basis of its indiscernibility from the label sets of the nearest neighbours of an instance, as well as the indiscernibility between these nearest neighbours and the unseen instance.
In addition, we will see that fuzzy rough sets can be used for preprocessing data:
  • Fuzzy Rough Feature Selection (FRFS) uses the dependency of the decision attribute on the fuzzy B-positive region, for varying attribute subsets B, to identify a decision superreduct.
  • Fuzzy Rough Prototype Selection (FRPS) uses the membership of instances in the lower and upper approximations of its decision class as a quality measure, and then heuristically choses a quality threshold that optimises classification accuracy.
Finally, we will also see how to use different OWA operators to make these results more robust to noise, and how to integrate approximative nearest neighbour searches to speed up query times with large datasets.



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Special session: Review of the Year. A Look back at Rough Sets by Davide Ciucci and Marcin Wolski

The goal of the session is to present the most interesting papers about rough sets and related topics, which have already been published between the annual IJCRS editions. Our goal is to bring influential authors who published their papers in prestigious scientific journals and allow them to present and discuss their research with a diverse lineup of rough set experts.
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 sub-session:
Back to the Future: Incoming Publications
Therefore very short papers, (2-4)-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 (2-4 pages long) of current research to: Important dates (see the CFP of this Special Session):
  • Submission deadline: 23 April
  • Acceptance notification: 30 April




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