Granular Computing for Evolving Explainable Models
Granular Computing for Evolving Explainable Models
A Special Issue of
A Special Issue of
Inspired by the way in which humans deal with complexity of real-world problems, Granular Computing is a computational paradigm devoted to create models from data that emphasize transparency, interpretability and scalability useful to develop efficient and explainable intelligent systems. Evolving granular models comprise an array of online modeling approaches capable of extracting knowledge from online data streams generated by nonstationary processes. They embody online learning methods and incremental algorithms that evolve or gradually change individual models to guarantee life-long learning and self-organization of the granular structure of the model.
Evolving granular models are based on Granular Computing, an information processing paradigm that embraces theories and methodologies of fuzzy set theory, rough set theory, interval analysis and alike to enable human-centered information processing. As such, it plays a fundamental role in the development of evolving artificial systems which have the distinction of leveraging explainable knowledge. Nowadays the importance of learning explainable models from data is outstanding (i) to improve the interaction between users and intelligent systems in order to tackle complex problems, (ii) to easily integrate artificial and human knowledge, and (iii) to allow users to validate the functionality of an intelligent system with respect to criteria of performance, ethics, safety, causality, etc., thus leading to the ultimate possibility of trusting artificial intelligent systems for mission-critical applications.
The special issue is intended to focus on the above aspects and will solicit papers that cover original research, overviews and applications of granular computing methods in the realm of evolving explainable intelligent systems.
Areas of interest include, but are not limited to:
Evolving Granular models for Data Streams and/or Big Data
Evolving Granular models for Explainable Artificial Intelligence
Foundations of Granular Computing for Evolving Explainable Models
Real-world applications of Evolving Explainable Models
Granular Computing and Fuzzy systems for Evolving models
Granular Computing and Neural/Neuro-fuzzy networks for Evolving Models
Granular and Evolutionary Computing for Evolving Models
Evolving solutions for real-time explainable models
Adaptive personal explainable systems
Explainable models for behavior analysis
Incremental Learning of explainable models for texts and document mining
Evolving granular models for document analysis
Explainable methods for opinion mining and sentiment analysis
Evolving methods for user profiling
Granular evolving methods for Human-computer interaction in explainable systems
Granular evolving methods for e-health
Granular evolving methods for smart cities
Industrial applications of explainable evolving systems
Papers will be screened by the guest editors and those deemed suitable will be sent to at least two reviewers. Manuscripts must apply the general author guidelines of the Journal, which are available at (https://www.springer.com/journal/12530/submission-guidelines) and must be submitted through the journal’s online submission portal (https://www.editorialmanager.com/evos/default.aspx).
Submissions open: August 1, 2020
Submissions deadline: December 15, 2020 January 15, 2021 (strict deadline)
Notification: February 15, 2021
Revision submission: March 15, 2021
Final notification: April 15, 2021
Publication: 4th quarter of 2021
Authors of accepted papers at the 2020 IEEE International Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS 2020) are invited to submit an extended version of their paper.
In addition, any other high-quality submission that fits the topics of this special issue is welcome. All invited papers will be subjected to the same rigorous review process as the regular submissions to this special issue. Submitted articles must not have been previously published or currently submitted for publication elsewhere. For work that has been published previously in a workshop or conference, it is required that submissions to the special issue report substantial advancements in research and have at least 40% of new content.
For any questions, please contact the Guest Editors
Giovanna Castellano (giovanna.castellano@uniba.it) - Ciro Castiello (ciro.castiello@uniba.it) - Corrado Mencar (corrado.mencar@uniba.it)
Department of Informatics - University of Bari “Aldo Moro” - Bari, Italy