Uncertainty Management in Intelligent Information Processing

Dear Colleagues,

Uncertain information exists in practical applications such as complex systems, fuzzy systems, robotics systems, risk analysis, fault diagnosis, supplier selection, knowledge-based systems, pattern recognition, classification, clustering, healthcare, and multi-attribute decision-making. Uncertain information in a system or process can be classified as fuzzy information, probabilities and imprecise probabilities that can be represented by Bayesian methods, fuzzy sets, ambiguity information because of discord or non-specificity in semantic inconsistency and incomplete information in the open world assumption.

How to manage the uncertainty in practical applications while performing information processing is emphasized in this Special Issue. For learning about uncertainty, we may use active learning, representation learning, semi-supervised learning, classification, clustering, deep learning and so on. For the representation of uncertainty, we may use probability theory, imprecise probabilities, rough set theory, random set theory, imprecise set theory, Dempster–Shafer evidence theory, fuzzy sets theory and so on. For the measuring of uncertainty, we may use Shannon entropy theory, belief entropy theory and so on. For the fusion of uncertainty, we may use the weighted average method, Kalman filter method, Bayesian estimation method, production rule, Dempster combination rule and so on.

This Special Issue aims to provide a forum for communicating knowledge related to concepts, theories, models and methods regarding uncertainty management in wide areas of practical applications using intelligent information processing algorithms, theories and methods. Papers may discuss uncertainty representation, fusion, measure, and applications with intelligent information processing theories and methods in all areas.

Dr. Yongchuan Tang
Prof. Dr. Deyun Zhou
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• uncertainty measure
• uncertainty management
• uncertainty in artificial intelligence
• intelligence information processing
• intelligent control
• information fusion
• Bayesian methods
• Monte Carlo methods
• Dempster–Shafer evidence theory
• fuzzy sets theory
• entropy
• belief entropy