Special Issue on Rough Sets and Insightful Reasoning Submission Date: 2024-09-30 The theory of rough sets serves as an easy-to-understand framework for data/information/knowledge representation and exploration. A number of rough set methods and algorithms have been designed with an emphasis on learning interpretable and insightful decision models from real-world data sources. This aspect has recently gained an additional importance because of a need of explainability of data-driven decision models. As a result, one can consider a new family of hybrid approaches whereby rough sets are combined together with AI/ML techniques focused on accuracy and performance. Discussion of such recent approaches states the primary goal of this special issue.

One says that a model is insightful, if it allows us for inferring practical knowledge about a real-world problem or phenomenon that it refers to. This differs from saying that a model is interpretable (it is possible to interpret how it works) or – which is now a particularly popular concept – explainable (it is possible to apply some additional methods that explain how it works). With this respect, the derivation of insightful models lays in the core of rough set methods for data exploration and KDD. Still, there are some other soft computing methods which pay similar attention to insightfulness too. Accordingly, the goal of this special issue is also to address hybrid AI/ML approaches that rely on such other soft computing paradigms, whereby – however – their relationship to rough sets should be elaborated.

Insightfulness is important not only for learning decision models (which is a domain of ML and data science) but also for a wider spectrum of computational and reasoning schemes (which is a general realm of AI). Our goal is therefore to discuss the usage of rough set principles also in such other types of schemes, which explains the second component of this special issue’s title – insightful reasoning. The particular example of such reasoning may refer to intelligent data acquisition or, more generally, the mechanisms that – actively and interactively – gather information that will be needed to learn, apply and maintain decision models in real-world scenarios. This topic is inspired by the research by Professor Andrzej Skowron who celebrated his 80th birthday anniversary at the 2023 International Joint Conference on Rough Sets (IJCRS 2023). This shows that rough sets can be used in complex, multi-level application frameworks and that such frameworks can be designed in an insightful way.

Guest editors:

Prof. Dominik Ślęzak (Executive Guest Editor)

University of Warsaw, POLAND;

Email: slezak@mimuw.edu.pl

Prof. Guoyin Wang

Chongqing University of Posts and Telecommunications, CHINA

Email: wanggy@cqupt.edu.cn

Prof. JingTao Yao

University of Regina, CANADA

Email: Jingtao.Yao@uregina.ca

Special issue information:

Topics of Interest:

Rough set and soft computing approaches to knowledge discovery and insightful data exploration

Rough set and soft computing methods for building explainable and interpretable AI/ML models

Rough set and soft computing methods for insightful monitoring and maintaining AI/ML models

Utilizing rough set principles in insightful computational models and reasoning models

Utilizing rough set principles in interactive complex data acquisition and active sensing

Utilizing rough set principles in complex process modeling and human-computer interaction

Manuscript submission information:

Tentative Dates:

Submission Open Date: February 7, 2024

Submission Deadline: September 30, 2024

Editorial Acceptance Deadline: February 28, 2025

Contributed full papers must be invited by the Guest Editors and submitted via the Information Sciences online submission system (Editorial Manager®). Please select the article type “VSI: Rough Sets and Insightful Reasoning” when submitting the manuscript online.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

rough sets; soft computing; data science; insightful reasoning about data; explainability and interpretability of data-driven models