CFP

IJCRS 2020: 2020 International Joint Conference on Rough Sets

Melia Habana Hotel

Havana, Cuba, June 29-July 3, 2020

Conference website	http://ijcrs.cujae.edu.cu
Submission link	https://easychair.org/conferences/?conf=ijcrs2020

Topics: rough sets fuzzy sets granular computing machine learning

New!

Due to the COVID-19 pandemic, we are hosting the conference as a virtual forum on the site

https://virtualijcrs2020.uclv.edu.cu/

on the same original dates, June 29 to July 3rd.

In light of this change, the Organizing Committee has decided that there will be NO registration fees for authors with accepted papers.

Stay tuned for more details on accessing the virtual forum and preparing presentation materials!

List of papers accepted in full versions (LNCS)

Patrick Doherty and Andrzej Szałas. Rough Forgetting
Marilyn Bello, Gonzalo Nápoles, Ricardo Sánchez, Koen Vanhoof and Rafael Bello. Feature Association Based on Granulation Entropy for Deep Neural Networks
Fulan Qian, Yafan Huang, Shu Zhao, Jie Chen, Xiangyang Wang and Yanping Zhang. HGAR: Hybrid Granular Algorithm for Rating Recommendation
Xue Rong Zhao and Duoqian Miao. Variable precision three-way concepts
Jie Chen, Yang Li, Shu Zhao, Xiangyang Wang and Yanping Zhang. Three-way Decisions Community Detection Model Based on Weighted Graph Representation
Peisen Li, Guoyin Wang and Jun Hu. Multi-granularity complex network representation learning
Zhiwen Jian, Hiroshi Sakai, Takuya Ohwa, Simon Shen and Michinori Nakata. Some Improvements of the NIS-Apriori based Rule Generator with Three-Way Decisions
Li Baizhen, Wei Zhihua, Zhang Hongyun, Zhang Nan and Sun Lijun. Quick Maximum Distribution Reduction in Inconsistent Decision Tables
Mani A. Functional Extensions of Knowledge Representation in General Rough Sets
Ivett Fuentes, Arian Pina, Gonzalo Nápoles, Leticia Arco and Koen Vanhoof. Rough Net Approach for Community Detection Analysis in Complex Networks
Mauricio Restrepo and Chris Cornelis. Attribute Reduction from Closure Operators and Matroids in Rough Set Theory
Leonardo Concepción, Gonzalo Nápoles, Rafael Bello and Koen Vanhoof. On the Behavior of Fuzzy Grey Cognitive Maps
Lingling Mao and Jingqian Wang. The reduct of a fuzzy β-covering
Andrea Campagner, Davide Ciucci and Valentina Dorigatti. Approximate Reaction Systems based on Rough Set Theory
Iliana Pérez Pupo, Pedro Yobanis Piñero Pérez, Roberto García Vacacela, Rafael Bello Pérez and Luis Albarado Acuña. Discovering fails in software projects planning based on linguistic summaries
M. Eugenia Cornejo, Jesús Medina and Eloísa Ramírez-Poussa. Algebraic structure of adjoint triples generating a weak negation on the unit interval
Nicolas Madrid and Eloisa Ramírez-Poussa. Representative set of objects in rough sets based on Galois connections
Tianlei Chen, Duoqian Miao and Yuebing Zhang. A Graph-based Keyphrase Extraction Model with Three-way Decision
Dávid Nagy, Tamás Mihálydeák and Tamás Kádek. Similarity Based Granules
Mengjun Hu. Concept Analysis Using Quantitative Structured Three-way Rough Set Approximations
Dominik Slezak and Agnieszka Chadzynska-Krasowska. Approximate Decision Tree Induction Over Approximately Engineered Data Features
Michinori Nakata, Hiroshi Sakai and Keitarou Hara. Rough Sets Based on Possible Coverings in Incomplete Information Tables
Roberto G. Aragón, Jesús Medina and Eloísa Ramírez-Poussa. On the hierarchy of equivalence classes provided by local congruences
Prosenjit Howlader and Mohua Banerjee. Object Oriented Protoconcepts and Logics for Double and Pure Double Boolean Algebras
Mani A. Towards Student Centric Rough Concept Inventories
Dominik Slezak and Sebastian Stawicki. The Problem of Finding the Simplest Classifier Ensemble is NP-Hard -- a Rough-Set-Inspired Formulation Based on Decision Bireducts
Istvan Harmati and Laszlo T. Koczy. On the Convergence of Input-Output Fuzzy Cognitive Maps
Iliana Pérez Pupo, Pedro Y. Piñero Pérez, Rafael Bello Pérez and Roberto Garcia Vacacela. Linguistic Summaries Generation with Hybridization Method based on Rough and Fuzzy Sets
Darian Horacio Grass Boada, Airel Pérez Suárez, Rafael Bello, Alejandro Rosete and Leticia Arco. Overlapping community detection using multi-objective approach and rough clustering
Jose L. Salmeron and Irina Arevalo. Distributed Artificial Intelligence for Cancer Research
Zbigniew Suraj, Aboul Ella Hassanien and Sibasis Bandyopadhyay. Weighted Generalized Fuzzy Petri Nets and Rough Sets for Knowledge Representation and Reasoning
Marko Palangetić, Chris Cornelis, Salvatore Greco and Roman Słowiński. Rough Sets Meet Statistics - A New View on Rough Set Reasoning about Numerical Data
María José Benítez Caballero, Jesús Medina and Eloisa Ramírez Poussa. Relating FCA extensions and alpha-blocks
Xin Cui, Yiyu Yao and Jingtao Yao. Modeling Use-Oriented Attribute Importance with the Three-Way Decision Theory
Sheela Ramanna and Rajesh Jaiswal. Detecting Overlapping Communities using Distributed Neighbourhood Threshold in Social Networks.
Andrea Campagner, Federico Cabitza and Davide Ciucci. Three-Way Decision for Handling Uncertainty in Machine Learning: a Narrative Review
Joanna Henzel, Andrzej Janusz, Marek Sikora and Dominik Slezak. On Positive-Correlation-Promoting Reducts

List of papers accepted in short versions

Dominik Slezak and Soma Dutta. The Concepts of Discernibility and Monotonicity in Attribute Reduction are not Equivalent to Each Other
Chi-Chang Chang. Predicting Risk Factors for Chronic Kidney Disease Progression

Previous information

Because of the extreme international situation caused by Covid-19:

1. The decisions about the acceptation of papers in IJCRS2020 will be sent to the authors on March 30.

2. Taking into account the situation related to Covid-19 the the conference will be moved to a virtual conference.

3. Joint to the decision about the papers, information about the organization of the virtual conference will be sent to the authors on March 30.

CFP IJCRS 2020

Rough set theory (RST) is a prominent methodology within the umbrella of and (GrC) to handle uncertainty in inconsistent environments. RST has enjoyed widespread success in a plethora of real-world application domains and remains at the forefront of numerous theoretical studies aiming at consolidating and augmenting its well-established properties.Computational IntelligenceGranular Computing

The International Joint Conference on Rough Sets (IJCRS2020) will take place in the Meliá Habana hotel in Havana, Cuba from June 29 to July 3, 2020. IJCRS 2020 aims at providing a forum for exchange on RST, GrC and their applications. The conference includes tutorials, invited key lectures and paper presentations.

IJCRS 2020's goals are to strengthen the relationships among researchers and institutions working on RST and GrC in general, to increase awareness of these topics and to facilitate the contact between new researchers and consolidated groups. Special interest will be paid to promoting RST among the Latin American research community.

IJCRS is the prime international conference sponsored by the International Rough Set Society (IRSS). IJCRS 2020 encapsulates four main tracks which refer to major threads of rough set conferences held so far:

Rough Sets and Data Science (in relation to RSCTC series organized since 1998)
Rough Sets and Granular Computing (in relation to RSFDGrC series organized since 1999)
Rough Sets and Knowledge Technology (in relation to RSKT series organized since 2006)
Rough Sets and Intelligent Systems(in relation to RSEISP series organized since 2007)

Important Dates

February 26, 2020 Paper submission deadline
March 30, 2020 Notification of acceptance (updated)
April 20, 2020 Camera-ready submission deadline (updated)
April 23, 2020 Submission deadline of Special session: Review of the Year. A Look back at Rough Sets
April 30, 2020 Acceptance notification of Special session: Review of the Year. A Look back at Rough Sets
April 19, 2020 Early-bird registration deadline
June 29 - July 3, 2020 IJCRS 2020 Conference

Submission Guidelines

Submissions of original and previously unpublished work on RST/GrC and applications are encouraged. All papers must be original and not simultaneously submitted to another journal or conference.

Submissions must be prepared in the LCNS/LNAI Springer format and have a maximum of 15 pages. Accepted papers will be published in the conference proceedings by Springer-Verlag in the LNCS/LNAI series, both in printed and digital forms.

If you intend to submit your paper to one of the special sessions (see list below), please add a checkmark besides the special session name when you list all the submission topics that are relevant to your work.

Moreover, short submissions can be submitted (3-6 pages), if accepted, these can be presented at the conference but will not be published in the proceedings.

Each submitted paper will be reviewed by three independent reviewers and the decision on its acceptance will be based on the results of these revisions.

All papers should be submitted through EasyChair.

Submission Topics

Core Rough Set Models and Methods:
Covering/Neighborhood-Based Rough Set Models,
Decision-Theoretic Rough Set Methods,
Dominance-Based Rough Set Methods,
Rough-Bayesian Models,
Rough Clustering,
Rough Computing,
Rough Mereology,
Rough-Set-Based Feature Selection,
Rule-Based Systems,
Partial Rough Set Models,
Game-Theoretic Rough Set Methods,
Variable Consistency / Precision Rough Sets,
Logic in Different Rough Set Models
Related Methods and Hybridization:
Artificial Intelligence,
Machine Learning,
Pattern Recognition,
Decision Support Systems,
Fuzzy Sets and Near Sets,
Uncertain and Approximate Reasoning,
Information Granulation,
Computing With Words,
Formal Concept Analysis,
Petri Nets,
Intelligent Agent Models,
Interactive Computing,
Nature-Inspired Computation Models,
Natural Language Processing,
Big Data Processing.
Areas of Applications:
Medicine and Health,
Bioinformatics,
Business Intelligence,
Telecommunications,
Smart Cities, Transportation,
Astronomy and Atmospheric Sciences,
Semantic Web, Web Mining and Text Mining,
Financial Markets, Retail and E-Commerce,
Computer Vision and Image Processing,
Cybernetics and Robotics,
Knowledge Discovery,
Knowledge Engineering and Representation,
Risk Monitoring

Special Sessions:
Fuzzy Logic, Formal Concept Analysis and Rough Sets (María Eugenia Cornejo, Dominik Ślęzak and Eloisa Ramírez-Poussa)
Fuzzy and Rough Cognitive Networks (Gonzalo Nápoles and László Kóczy)
Rough sets and Matroids (Mauricio Restrepo)
Review of the Year. A Look back at Rough Sets (Davide Ciucci and Marcin Wolski)
Tutorials:
Comparative approaches to granularity in general rough sets (A. Mani)
Fuzzy rough set classification techniques (Oliver Urs Lenz)

Invited speakers:
Explainable AI: From Data to Symbols and Information Granules (Witold Pedrycz)
How indiscernibility and similarity make life easier (Dominik Ślęzak)

Committees

Steering Committee

Davide Ciucci (University of Milano-Bicocca, Italy)
Tamás Mihálydeák (University of Debrecen, Hungary)
Victor Marek (University of Kentucky, USA)
Sushmita Mitra (Indian Statistical Institute, India)

Organizing Committee

General Co-Chairs: Rafael Bello (Central University of Las Villas, Cuba) and Duoqian Miao (Tongji University, China)
Local committee chair: Alejandro Rosete (Universidad Tecnológica de La Habana José Antonio Echeverría CUJAE, Cuba)
Technical Program Committee Co-Chairs: Rafael Falcon (University of Ottawa, Canada) and Michinori Nakata (Josai International University, Japan)
Special Session Co-Chairs: Chris Cornelis (Gent University, Belgium) and Hong Yu (Chongqing University of Posts and Telecommunications, China)
Publicity Chair: Mauricio Restrepo (Universidad Militar Nueva Granada, Colombia)
Honorary Chairs: Andrzej Skowron (Warsaw University, Poland) and Yiyu Yao (University of Regina, Canada)

Technical Program Committee (in alphabetical order)

Amedeo Napoli, LORIA Nancy (CNRS - Inria - Université de Lorraine) France
Andrei Paun, University of Bucharest
Andrzej Szałas, University of Warsaw
Andrzej Skowron, Warsaw UIniversity
Anna Gomolinska, University of Bialystok, Institute of Informatics
Bay Vo, Ho Chi Minh City University of Technology, Ho Chi Minh, Viet Nam
Beata Zielosko, Univeristy of Sielsia, Institute of Computer Science
Bing Zhou, Sam Houston State University
Caihui Liu, Gannan Normal University
Chien-Chung Chan, University of Akron
Christopher Hinde, Loughborough University
Churn-Jung Liau, Academia Sinica, Taipei, Taiwan
Claudio Meneses, Universidad Católica del Norte
Costin-Gabriel Chiru, "Politehnica" University from Bucharest
Davide Ciucci, Università di Milano-Bicocca
Dayong Deng, Zhejiang Normal University
Dmitry Ignatov, National Research University Higher School of Economics
Dongyi Ye, Fuzhou University
Georg Peters, Munich University of Applied Sciences & Australian Catholic University
Guilong Liu, Beijing Language and Culture University
Guoyin Wang, Chongqing University of Posts and Telecommunications
Hiroshi Sakai, Kyushu Institute of Technology
Hung Son Nguyen, Institute of Mathematics, The University of Warsaw
Ivo Düntsch, Brock University
Jaroslaw Stepaniuk, Bialystok University of Technology
Jaume Baixeries, Universitat Politècnica de Catalunya
Jesús Medina, University of Cádiz
Jingtao Yao, Department of Computer Science. University of Regina
Jiye Liang, Shanxi University
Jouni Jarvinen, Department of Mathematics and Statistics, University of Turku
Krzysztof Pancerz, University of Rzeszow, Poland
Loan T. T. Nguyen, University of Warsaw
Mani A, HBCSE, Tata Institute of Fundamental Research, India
Marcin Szczuka, Institute of Informatics, The University of Warsaw
Marcin Michalak, Silesian University of Technology
Marek Sikora, Silesian University of Technology
Marzena Kryszkiewicz, Warsaw University of Technology
Masahiro Inuiguchi, Osaka University
Md. Aquil Khan, Indian Institute of Technology Indore
Michal Kepski, University of Rzeszow
Michinori Nakata, Josai International University
Mohua Banerjee, Indian Institute of Technology Kanpur
Mu-Chen Chen, National Chiao Tung University
Murat Diker, Hacettepe University
Nguyễn Long Giang, Viện Công nghệ thông tin
Nizar Bouguila, Concordia University
Piotr Artiemjew, University of Warmia and Mazury
Pradipta Maji, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
Rafal Gruszczynski, Nicolaus Copernicus University in Torun
Richard Jensen, Aberystwyth University
Ryszard Janicki, McMaster University
Ryszard Tadeusiewicz, AGH University of Science and Technology, Krakow, Poland
Sándor Radeleczki, Department of Analysis, University of Miskolc
Sheela Ramanna, Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba R3B 2E9 Canada
Soma Dutta, University of Warmia and Mazury in Olsztyn, Poland
Tamás Mihálydeák, University of Debrecen
Thierry Denoeux, Universite de Technologie de Compiegne
Tianrui Li, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China
Vilem Novak, University of Ostrava
Vladimir Parkhomenko, Peter the Great St.Petersburg Polytechnic University
Wojciech Ziarko, University of Regina
Xiuyi Jia, School of Computer Science and Technology, Nanjing University of Science and Technology
Yan Yang, School of Information Science & Technology, Southwest Jiaotong University
Yiyu Yao, University of Regina
Yoo-Sung Kim, Inha Univ.
Zbigniew Suraj, Chair of Computer Science, University of Rzeszów, Rzeszów, Poland
Zbigniew Ras, UNC Charlotte
Zied Elouedi, Institut Supérieur de Gestion de Tunis
Zoltán Ernő Csajbók, University of Debrecen

Publication

IJCRS 2020 proceedings will be published by Springer-Verlag in the LNCS/LNAI series, both in printed and digital forms.

Venue

The conference will be held in the Melia Habana Hotel in Havana, Cuba

Contact

For questions regarding paper submission and publication, please email the TPC Co-Chairs: Rafael Falcon (rfalcon@ieee.org) and Michinori Nakata (nakatam@ieee.org)
For questions regarding travel arrangements, please email the Local Arrangements Chair: Alejandro Rosete (rosete@ceis.cujae.edu.cu)

Preliminary program

Conference: Explainable AI: From Data to Symbols and Information Granules by Witold Pedrycz

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.

Conference: How indiscernibility and similarity make life easier by Dominik Ślęzak

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.

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.
Primitive, precision-based, axiomatic 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
Mereology, High granular operator spaces and variants
Problems of granular computing, hybrid contexts, and adaptivity

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.

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.

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 PublicationsTherefore 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:

Davide Ciucci: davide.ciucci@unimib.it
Marcin Wolski: maarten.wolski@gmail.com

Important dates:

Submission deadline: 23 April
Acceptance notification: 30 April

Important Dates

Submission Guidelines

Submission Topics

Core Rough Set Models and Methods:

Related Methods and Hybridization:

Areas of Applications:

Special Sessions:

Tutorials:

Invited speakers: